1. No category

Title
Author(s)
Incidence, predictors and implications of depression after
stroke
Lee, Chu-kee, Angel.; 李珠璣.
Citation
Issue Date
URL
Rights
2008
http://hdl.handle.net/10722/53151
The author retains all proprietary rights, (such as patent
rights) and the right to use in future works.
Incidence, Predictors and Implications of
Depression After Stroke
by
Angel Chu Kee LEE
RN BN(Hon) MBA in HSM MMedSc
A thesis submitted in partial fulfillment of the requirements for the Degree of
Doctor of Philosophy at The University of Hong Kong
February 2008
Abstract of thesis entitled
“Incidence, Predictors and Implications of
Depression After Stroke”
Submitted by
Angel Chu Kee LEE
for the degree of Doctor of Philosophy
at The University of Hong Kong
on February 2008
Background
Stroke after depression (DAS) in old age, in particular, casts a triple burden in
healthcare (stroke, depression, old age). However, DAS is generally under-recognized.
There is a paucity of studies on the incidence and predictors of DAS among the older
Chinese populations, possibly due to the logistic difficulty in evaluating depression in
older subjects of limited educational background with cognitive and physical
impairment resulted from stroke. This thesis serves to address some unanswered
important issues related to DAS in Hong Kong Chinese, particularly in the area of
incidence, evaluation, assessment, research and educational needs.
Aim and objectives
The research is aimed to: (1) estimate the incidence of DAS among Chinese
subjects who have first-ever ischemic stroke, (2) identify the risk factors and
predictors for DAS, (3) evaluate the changes in various functional domains among
stroke survivors at one month and six months, (4) discuss the clinical outcome of the
depressed stroke survivors, and (5) recommend directions for research, education and
practice in DAS.
i
Methods
This study adopted a descriptive longitudinal study design. Consecutive ischemic
stroke patients were recruited, and interviewed at one month and six months after
stroke. A single-blind psychiatric consultation with all depressed subjects and a
sample of non-depressed subjects was also conducted after the six-month interview. A
6-section questionnaire examined variables from five domains - bio-anatomical,
cognitive and communicative, dependency and somatic symptoms, emotional, and
family and social support. Depression was assessed by DSM IV, GDS, CESD, and
also by three smiley diagrams.
Results
DAS occurred one in four at one month and one in ten at six months. The
determinants for DAS at one month fell into the emotional and dependency-level
domains. Cognitive function was also a determinant at six months. Emotional,
cognitive, and somatic symptoms domains at one month predicted depression score,
and jointly explained a 44% variability of DAS at six months. Majority of the stroke
participants did not know ‘what is depression’ in Chinese. 8.5% of the participants (16
out of 188) had consulted psychiatrists at six months.
DAS was found to be common among Chinese. Life stressors, display of sad
faces, early cognitive decline and unresolved somatic symptom are important warning
signs for DAS in the long-term. Chinese older stroke patients express depression in
words not commonly used by clinicians in depression diagnosis. While diagnosis
using standard Western diagnostic criteria could be more accurate, the simple Smiley
diagrams, possibly a culturally neutral, less time-consuming and less dependent on
verbal ability and educational background, might be suitable for preliminary screening
of DAS in these older stroke patients. Careful interpretation of DAS assessment results
is needed because Chinese patients may moderate emotional expressions.
ii
Significance
This study provides updated information of DAS in older Chinese populations.
A high incidence of DAS is found but under-recognition, under-detection and
under-treatment of DAS are also common. Management of these patients requires
more than just prescribing antidepressant drugs when warning signs of depression are
observed. Many psycho-social issues detected in the study may contribute useful
reference for future healthcare planning, research, practice and education in the local
setting.
iii
Declaration
I declare that this thesis represents my own work, except where due acknowledgement
is made, and that it has not been previously included in a thesis, dissertation or report
submitted to this University or to any other institution for a degree, diploma or other
qualifications.
Signed ………………………………
iv
Acknowledgements
I am much indebted to my supervisor, Professor S W Tang - an eminent scientist,
a dedicated scholar, a tireless worker, a teacher who guides his students by facilitation
rather than prescriptions and lectures, a patient-Centerd clinician, a person with
integrity, wisdom and, most important, an all-encompassing heart.
My sincere gratitude also goes to Dr Gabriel Yu, my second supervisor, and to
Prof Sophia Chan, Head of the Department of Nursing Studies. Without their support,
my doctoral study could not have become a reality. During the research process, I have
learnt much from the generous teaching of Professor R T F Cheung and Dr Daniel
Fong.
Many clinical partners have offered me constant support, and I must thank in
particular Dr T H Tsoi, Dr H C Ma, Mr K H Lo, Ms Lucy Cheng, Ms Virgina Cheung,
Mr David Wong, Ms Erica Yeung, Mr Eric Kuk, Dr C M Chang and many others. I am
grateful for the research assistance offered by Carmen, Elaine, Jacky, Joanna and Ting.
Last but far from least, I am very much appreciative of all stroke participants and
their families, whose sharing has enlightened my understanding of depression after
stroke. Their suffering and resilience during the disease process have given me much
insight into future research, practice and education in healthcare.
The project was funded by The University of Hong Kong Seeding Funding.
v
香港大學醫學院精神科教授 鄧兆華 醫生惠存
鄧
兆
華
香
港
大
學
精
神
科
教
授
德
澤
中
華
醫
德
彩
薰
中
雅
貫
研
工
學
誨
導
範
福
外
夏
桃
明
生
痌
瑞
君
中
醫
妙
論
諄
學
唐
病
杏
寰
李
珠
璣
瘝
龍
子
西
術
手
證
諄
梓
堯
黎
林
宇
天
譽
高
和
光
蔭
良
育
濟
樹
盡
攀
承
除
齊
沐
下
豐
瞻
風
社
蒼
師
青
萬
樑
關
頂
仲
苦
讚
春
受業
重
隆
矚
送
會
穹
表
松
民
棟
懷
峰
景
痛
賞
風
題謝
二零零八年
李珠璣
vi
Dedication
To Leung Shing On and Leung Ka Yan - my drives in life.
To God - my destiny in life.
vii
Contents
Abstract………………………………………………………………………………………… i
Declaration…………………………………………………………………….…………… iv
Acknowledgements…………………………………………………………………………… v
Table of Contents……………………………………………………………………………viii
List of Tables…………………………………………………………………………………xiv
List of Figures …………………………………………………………………. ………… xvi
List of Appendices……………………………………………………………….………… xvii
Abbreviations……………………………………………………………………………… xviii
Publications………………………………………………………………………………… xxi
Chapter 1
Introduction…………………………………………………………………… 1
Chapter 2
Methods…………………………………………………………………… 29
Chapter 3
Results……………………………………………………………………… 58
Chapter 4
Discussion………………………………………………………………… 128
Chapter 5
Summary and conclusions………………………………………………… 175
References………………………………………………………………………… 183
Appendices……………………………………………………………………… 202
viii
Chapter 1 Introduction
1.1
Background
1.1.1
Definitions
1.1.2
Significance of the problem
1.1.3
Importance of the study
1.2
Literature review
1.2.1
Ischemic stroke in Hong Kong
1.2.1.1
Impact of stroke
1.2.1.2
Interventions and rehabilitation for stroke survivors
1.2.2
Geriatric depression
1.2.2.1
Assessment and prevalence of geriatric depression
1.2.2.2
Factors associated with geriatric depression
1.2.2.3
Interventions for geriatric depression
1.2.3
Depression after stroke (DAS)
1.2.3.1
Impact of DAS
1.2.3.2
Incidence and prevalence of DAS
1.2.3.3
Risk factors and predictors of DAS
1.3
y
Bio-anatomical domain factors
y
Cognitive and communicative domain
y
Dependency and somatic symptom domain
y
Emotional domain
y
Family and social support domain
Indications for current study
1.3.1
Gaps of previous studies
1.3.2
Research questions
ix
Chapter 2 Methods
2.1
Aim and objectives of study
2.2
Study design
2.2.1
Justification of study design
2.2.2
Ethics approval and consent
2.2.3
Study procedures
2.2.3.1
Record reviews
2.2.3.2
Interviews
2.3
Sampling
2.3.1
Target populations
2.3.1.1
Inclusion criteria
2.3.1.2
Exclusion criteria
2.3.2
Sampling method
2.3.3
Sampling size
2.4
Data collection
2.4.1
Study outcomes - the incidence and predictors of DAS
2.4.1.1
Tools for diagnosing and quantification of depression in patients
suspected of DAS
2.4.1.2
Use of smiley diagrams to detect depression in patients suspected
of DAS
2.4.1.3
Health literacy regarding depression
2.4.1.4
The domain factors associated with DAS and predictors of DAS
2.4.2
DAS interventions
2.4.3
Pilot study
2.5
Data analysis
2.5.1
Data management
2.5.2
Patients’ demographic and health profiles
2.5.3
Validity of the assessment tools
2.5.4
Incidence of DAS at one month and six months
2.5.5
Risk factors and predictors of DAS
2.5.6
Changes of domain factors from one month to six months
2.5.7
The clinical outcomes of stroke survivors
2.5.8
Qualitative analysis – depression literacy
x
Chapter 3 Results
3.1
Subject recruitment
3.1.1
Inclusion and exclusion analysis
3.1.2
Representativeness of recruited sample
3.1.3
Descriptive analysis of participants
3.1.3.1
Demographics and medical history
3.1.3.2
Bio-anatomical characteristics of stroke lesions
3.1.3.3
Cognitive and communicative status
3.1.3.4
Dependency level and somatic complaints
3.1.3.5
Emotional status
3.1.3.6
Family and social support
3.2
Study outcomes
3.2.1
Level of agreements between depression assessments
3.2.1.1
The level of agreement between psychiatrists and the research
nurse
3.2.1.2
The level of agreement between depression assessment tools
3.2.2
Incidence of DAS
3.2.3
Risk factors of DAS
3.2.3.1
Using DSM IV as the assessment tool
3.2.3.2
Using GDS/CESD as the assessment tool
3.2.4
Predictors of DAS
3.2.4.1
Using DSM IV as the assessment tool
3.2.4.2
Using GDS/CESD as the assessment tool
3.2.5
Health literacy of depression
3.2.5.1
Descriptors in affective domain
3.2.5.2
Descriptors in behavioural domain
3.2.5.3
Descriptors in cognitive domain
3.3
Clinical outcomes
3.3.1
Post-stroke quality of life
3.3.2
Institutionalisation and mortality
3.3.3
DAS interventions in formal healthcare
3.3.3.1
Use of anti-depressants
3.3.3.2
Use of mental healthcare resources
xi
Chapter 4 Discussion
4.1
Incidence of DAS at one month and six months
4.1.1
Comparing the incidence with published Chinese samples
4.1.2
Comparing the incidence with published Caucasian samples
4.1.3
The trend of DAS at six months and after
4.2
Risk factors and predictors of DAS
4.2.1
Bio-anatomical domain
4.2.2
Cognitive and communicative domains
4.2.3
Dependency and somatic symptoms domain
4.2.4
Emotional domain
4.2.5
Family and social support domain
4.2.6
Demographic and other factors
4.3
DAS interventions
4.3.1
Consequence of inattention to DAS
4.3.2
Primary preventive measures
4.3.2.1
Recognition of DAS - depression literacy
4.3.2.2
Early detection of DAS – use of smiley diagrams
4.3.3
Treatment modalities of DAS
4.3.3.1
Pharmacological treatment
4.3.3.2
Non-pharmacological treatment
4.4
Limitations of study
4.4.1
Threats to internal validity
4.4.1.1
Selection bias
4.4.1.2
Study design
4.4.1.3
History
4.4.1.4
Maturation
4.4.1.5
Mortality
4.4.2
Threats to external validity
4.4.2.1
Sample representativeness
4.4.2.2
Reliability of reporting ‘depression’ in Chinese stroke patients in
Hong Kong hospitals
4.4.2.3
Measurement effects
xii
4.5
Recommendations for future research, practice and education
4.5.1
Future research
4.5.1.1
Incidence studies
4.5.1.2
Bio-anatomical or biochemical research
4.5.1.3
Depression literacy studies
4.5.1.4
Detecting depression
4.5.2
Future practice and education
Chapter 5 Summary and conclusions
References
Appendices
Publications
xiii
List of tables
Table 1.1
Prevalence of GD in Hong Kong (13)
Table 1.2
Conceptual framework in postulating DAS causality (21)
Table 2.1
Comparing the profile of sampling frame with other districts in Hong
Kong (38)
Table 2.2
Sampling estimation using Epinfo 2005 (40)
Table 2.3
Process review agenda on DAS (50)
Table 3.1
Reasons for admission and exclusion of study subjects (61)
Table 3.2
Comparing age and gender groups of potential participants (62)
Table 3.3
Representativeness of the recruited sample (62)
Table 3.4
Demographic characteristics of 214 participants (64)
Table 3.5
Medical history of 214 participants (65)
Table 3.6
Characteristics of stroke lesions among participants at one month (66)
Table 3.7
Functional levels in cognitive and communicative domains at one
month and six months (69)
Table 3.8
Dependency level and somatic symptoms at one month and six
months (72)
Table 3.9
Emotional status at one month and six months (77)
Table 3.10
Frequency of displaying different facial expressions over the past
week at one month and six months (79)
Table 3.11
Coping resources (80)
Table 3.12
The level of agreement between psychiarists’ and nurse’ assessment
on depression (82)
Table 3.13
Screening property of depression assessment tools at one month (83)
Table 3.14
Incidence of depression at one month and six months (85)
xiv
Table 3.15
Changes in the GDS and CESD mean scores at one month and six
months (85)
Table 3.16
Relationship of socio-demographics and health history with DAS (89)
Table 3.17
The association of bio-anatomical domain factors with DAS at one
month (90)
Table 3.18
The association of cognitive and communicative domain factors with
DAS (92)
Table 3.19
The association of dependency and somatic symptom domain factors
with DAS (93)
Table 3.20
The association of emotional domain factors with DAS (97)
Table 3.21
The association of social support domain factors with DAS at six
months (99)
Table 3.22
Factors associated with GDS at one and six months (101)
Table 3.23
Factors associated with CESD at one and six months (103)
Table 3.24
Selected variables for regression analysis in DSM (from Tables
3.17-3.21) (107)
Table 3.25
Determinants of DAS assessed by DSM IV (109)
Table 3.26
Determinants of DAS assessed by GDS (109)
Table 3.27
Determinants of DAS assessed by CESD (110)
Table 3.28
Predictors of depression scores (by GDS) at six months (113)
Table 3.29
Predictors accounting for the change in depression scores (by GDS)
from one month to six months (113)
Table 3.30
Comparing demographic and depression between of depression
literate and illiterate groups (114)
xv
Table 3.31
Domains of emerging themes on depression interpretation by 86
participants (114)
Table 3.32
Analysis of ‘Burying things in oneself and concealing from others’
under affective domain (117)
Table 3.33
Analysis of ‘Nature and content of thoughts’ under cognitive domain
(120)
Table 3.34
Predictors of quality of life at six months (122)
Table 3.35
Treatment and clinical outcomes of the participants (125)
Table 3.36
Comparison of clinical status and outcome between dropouts and
persistent participants (127)
Table 4.1
Comparing the incidence findings with published studies (131)
xvi
List of figures
Figure 3.1
A flow chart of subject recruitment (60)
Figure 3.2
Changes in Quality of life score from one month to six months (122)
xvii
List of appendices
Appendix 2.1
Timetable for the research project
Appendix 2.2
DAS consent form
Appendix 2.3
Interview guide for psychogeriatrician
Appendix 2.4
1M questionnaire
Appendix 2.5
6M questionnaire
Appendix 2.6
Comparison of age and gender between two target hospitals
Appendix 3.1
Bio-chemical status of participants at 1M after stroke admission
Appendix 3.2
Individual item score in DSM at 1M and 6M
Appendix 3.3
Individual item score in GDS at 1M and 6M
Appendix 3.4
Individual item score in CESD at 1M and 6M
Appendix 3.5
Correlation of risk factors with GDS at 1M and 6M
Appendix 3.6
Correlation of risk factors with CESD at 1M and 6M
xviii
Abbreviations
1M
6M
ACA
AMT
ANOVA
BADL
BDI
C&AH
CA
CBT
CD
CESD
CI
CO
CSSA
CT scan
CVA
DA
DAS
DBP
df
DM
DSM IV
EC
EH
F
FBS
FN
FP
GCS
GD
GDS
GHQ
HA
HAD
HDA
HCP
HT
ICD
Inc
INR
LOD
LSNS
MAOI
Min
Max
MMSE
MRA
One month
Six months
Anterior Cerebral Artery
Abbreviated Mental Test
Analysis of Variance
Barthel Activity of Daily Living
Beck Depression Inventory
Care and Attention Home
Carotid Artery
Cognitive Behavior Therapy
Carotid Doppler
Center of Epidemiologic Studies of Depression
Confidence Interval
Cut-off point
Combined Social Security Allowance
Computerized tomography
Cardiovascular Accident/Stroke
Disability Allowance
Depression After Stroke
Diastolic Blood Pressure
degree of freedom
Diabetes Mellitus
Diagnostic and Statistical Manual, Fourth Edition
Elderly Center
Elderly Home
Test statistic used in ANOVA and other tests
Fasting Blood Sugar
False Negatives
False Positives
Glasgow Coma Scale
Geriatric Depression
Geriatric Depression Scale
General Health Questionnaire
Hospital Authority
Hospital Anxiety and Depression Scale
High disability Allowance
Health Care Professional
Hypertension
International Codes of Diagnoses
Incidence
International Normalized Ratio
Level Of Disturbance
Lubben Social Network Scale
Monoamine Inhibitor
Minimum
Maximum
Mini Mental State Examination
Magnetic Resonance Angiography
xix
MRI
MRSQoL
n
N
NA
NAD
NH
NS
NSPS
OAA
OR
p
PACI
PCA
PG
POCI
PTSD
QoL
r
R
R2
SBP
sc
SD
Sen
Spe
SSRIs
t
TACI
TIA
TN
TP
TCD
US
χ2
Magnetic Resonance Imaging
Modified Rankin Scale for Quality of Life
Number of cases in a subgroup or the sample
Total number of cases or sample members
Not applicable
No abnormality detected
Nursing Home
Not Significant
Norton Scale of Pressure Sore
Old Age Allowance
Odds ratio
p-value; probability that observed data are consistent with null
hypothesis
Partial Anterior Circulation Infarction
Posterior Cerebral Artery
Psychogeriatrician
Posterior Circulation Infarction
Post-traumatic stress disorder
Quality of Life
Pearson product-moment correlation coefficient
Multiple correlation coefficient
Coefficient of determination, proportion of variance in Y attributable
to Xs
Systolic Blood Pressure
subject code
Standard Deviation
Sensitivity
Specificity
Selective Serotonin Reuptake Inhibitors
t-value
Total anterior circulation ischemia
Transient Ischaemic Attack
True Negatives
True Positives
Transcranial Carotid Doppler
United States
Greek chi-squared, a test statistic for several nonparametric tests
xx
Publications
Lee, A. C. K., Tang, S. W., Yu, G. K. K., Cheung, R. T. F. (2007). Depression after
stroke: incidence and predictors. International Journal of Psychiatry in Clinical
Practice 11, (3):200-206.
Lee, A. C. K., Tang, S. W., Yu, G. K. K., Cheung, R. T. F. (2007). The Smiley as a
Simple Screening Tool for Depression After Stroke: a preliminary study. International
Journal
of
Nursing
Studies
(in
press).
Retrievable
online,
DOI:
10.1016/j.ijnurstu.2007.05.008.
xxi
Chapter 1 Introduction
1.1
Background (2)
1.1.1 Definitions (3)
1.1.2 Significance of the problem (3)
1.1.3 Importance of the study (4)
1.2
Literature review (6)
1.2.1 Ischemic stroke in Hong Kong (6)
1.2.1.1
Impact of stroke (6)
1.2.1.2
Interventions and rehabilitation for stroke survivors (8)
1.2.2 Geriatric depression (10)
1.2.2.1
Assessment and prevalence of geriatric depression (11)
1.2.2.2
Factors associated with geriatric depression (13)
1.2.2.3
Interventions for geriatric depression (14)
1.2.3 Depression after stroke (DAS) (16)
1.2.3.1
Impact of DAS (17)
1.2.3.2
Incidence and prevalence of DAS (18)
1.2.3.3
Risk factors and predictors of DAS (20)
y
1.3
Bio-anatomical domain factors
„
Anatomical hypothesis
„
Vascular hypothesis
„
Biochemical hypothesis
y
Cognitive and communicative domain
y
Dependency and somatic symptom domain
y
Emotional domain
y
Family and social support domain
Indications for current study (28)
1.3.1 Gaps of previous studies (28)
1.3.2 Research questions (28)
1
1.1 Background
Stroke, or cerebrovascular disease, is prevalent worldwide (American Stroke
Association 2005) and also in Hong Kong. It has been ranked the third or fourth most
deadly disease (Center of Health Protection 2006). Nearly one in ten admissions are
due to stroke every year (Department of Community Medicine 1998). Moreover,
depression is especially important among the older population because of their high
vulnerability (Woo et al. 1994, Zalewski et al. 1994) and the tragic outcomes
depression brings (Yip et al. 1998). It has been predicted that depression will become
the second most burdensome global disease in the next two decades (Murray & Lopez
1996). The prevalence of depression within the geriatric population alone ranged from
10% to 40% among samples from clinical or institutional settings (Leung 1998) as
well as from the community at large (Woo et al. 1994, Zalewski et al. 1994, Lee 2003).
The lifetime prevalence of depression can be as high as 60% in Australia (National
Mental Health Strategy Evaluation Steering Committee 1997).
Historically, the earliest papers introducing depression after stroke (DAS) dated
back to 1982 and later (Feibel & Springer 1982, Robinson et al. 1985, House 1987,
Primeau 1988). Some authors admitted that there had been few systematic studies on
DAS despite frequent documentation of psychological symptoms among stroke
survivors (Catapano & Galderisi 1990, Diller & Bishop 1995). Studies had usually
been done on a small scale with small numbers of stroke participants (Egelko et al.
1989, Schubert et al. 1992a). A cross-sectional study design had been commonly used,
and so a causal relationship between stroke and depression had yet to be confirmed
(Schubert et al. 1992a).
To identify the temporal link between stroke and depression, a longitudinal study
design has been undertaken more frequently from the 90s onwards (Bacher et al. 1990,
Morris et al. 1990, Andersen et al. 1994), and studies on depression and stroke
collectively have increased in number (Robinson et al. 1990, Andersen et al. 1994,
O'Rourke et al. 1998). Both discrete reports (Cushman 1988, Egelko et al. 1989,
Morris et al. 1990, Francisco 1993, Agrell & Dehlin 1994) and some national samples
2
estimate the prevalence of depression after stroke among first or recurrent stroke
patients to be between 10 and 27% (National Institute of Mental Health 2002).
However, there is a paucity of studies on the incidence and predictors of DAS among
Chinese populations, despite the high admission rate of stroke patients and the fact that
stroke is one of the top five killing diseases in mortality statistics. The applicability of
results from abroad regarding the prevalence and incidence of the risk factors or
predictors (and the controversy over them) remains largely unknown, not to mention
evidence-based interventions. This thesis serves to address some of the unanswered
important issues related to DAS in Hong Kong Chinese, particularly in the area of
incidence, evaluation, assessment, educational needs and welfare.
1.1.1 Definitions
According to the American Stroke Association (2007), stroke is a type of
cardiovascular disease. It affects the arteries leading to and within the brain. A stroke
occurs when a blood vessel that carries oxygen and nutrients to the brain is blocked by
a clot or bursts. When that happens, part of the brain cannot get the blood (and oxygen)
it needs, so it starts to die.
Depression is a serious medical illness, not imaginary or a weakness of one’s
personality as some would like to believe. It is more than just feeling ‘down in the
dumps’ or ‘blue’ for a few days. It is feeling ‘down’ and ‘low’ and ‘hopeless’ for weeks
at a time (National Institute of Mental Health 2007).
1.1.2 Significance of the problem
Depression after stroke (DAS) is a triple burden itself, actually combining three
conditions: stroke, depression and old age. DAS will likely demand an intensive
healthcare effort. The three conditions have been studied extensively, in a discrete way,
at national and global levels (Murray & Lopez 1996, Nussbaum 1997, O'Connell et al.
2004, American Stroke Association 2005). Studies of DAS as a three-in-one
phenomenon, however, have been comparatively less frequent.
3
Stroke is the third most frequent cause of mortality among Caucasians (Hinkle
1998, American Stroke Association 2005) and ranks third/fourth locally (Yu et al.
2003, Center of Health Protection 2006). In Hong Kong, stroke accounted for more
than 25,700 in-patient discharges and deaths in all hospitals and 3 434 deaths in 2005
(Center of Health Protection 2005, 2006). Stroke is known to cause tremendous
suffering and dysfunction in various domains, with recovery a tedious process.
Depression has caused growing concern in many societies because of its high
life-time prevalence and the projection that it will become the second global burden of
disease (Murray & Lopez 1996, Strock 2000, National Institute of Mental Health
2003). Compared with other age groups, older adults are found to be more susceptible
to depression in Caucasian samples (Zalewski et al. 1994, Commonwealth Department
of Health and Aged Care 2000, National Institute of Mental Health 2003), and Chinese
are no different (Woo et al. 1994, Leung 1998, Lee 2003).
Chinese constitute one fifth of the global population. The ageing of the Chinese
population is an imminent and inevitable challenge to healthcare, with one in four
residents aged over 60 by 2030 according to projected health and vital statistics in
Hong Kong (Department of Health Census and Statistics 2003). Naturally, ageing is
associated with increased health concerns and healthcare expenditure.
1.1.3 Importance of the study
The study of DAS remains an important public health topic. Public health
concerns are raised over a certain health problem according to a few criteria: (1) it is
common, as evidenced by high incidence and prevalence among the population, (2)
the problem is a chronic phenomenon that is not resolved without active intervention,
(3) it imposes a tremendous burden on healthcare, the victims themselves and society
at large, (4) it is detectable, treatable and preventable. The justifications of DAS as a
significant health problem are as follows:
1.
DAS may cause multiple impacts and tremendous burdens on patients,
families and the healthcare system.
4
2.
There are discrepancies in the existing findings on incidence and predictors
of DAS.
3.
There is a paucity of information regarding incidence and predictors of
DAS among Chinese subjects in any Chinese community around the world
including China.
4.
DAS exemplifies how the intercalated health problem of a multifaceted
disease etiology can be understood and delineated systematically and
studied chronologically from acute to chronic stages, establishing the
natural history of the problem.
5.
DAS study helps to explore the gaps in care and management of a highly
debilitating disease by reflecting on the domains of unmet needs
5
1.2 Literature review
1.2.1 Ischemic stroke in Hong Kong
Stroke or cardiovascular accident (CVA) is one of the leading five causes of
mortality in Hong Kong (Center of Health Protection 2005, 2006). It is considered the
most important and frequent condition in the specialty of neurology (Yu et al. 2003).
But it is not a homogeneous disease, with 70% being of the ischemic type and 30%
originating in haemorrhagic lesions. Without due clinical attention, stroke may lead to
high mortality, morbidity and disability (Robinson 1997, Chang et al. 1999, Yu et al.
2003).
In Hong Kong, most acute stroke patients are admitted to hospital wards in the
general medical, neuro-medical or stroke units, depending on hospital policy or
availability. Admission or transfer to the neurosurgery division takes place if
neurosurgical intervention is contemplated. Common investigations and treatment for
a suspected stroke patient include: non-contrast computed tomography scan of the
brain within 24 hours of first admission, consideration of thrombolytic therapy,
identification and treatment of risk factors and complications, as well as the early
rehabilitation programme (Tsoi 2004). After stabilisation of the clinical status, stroke
patients may be transferred to a rehabilitation hospital as in-patients or discharged
home with or without an active rehabilitation programme to be undertaken at day
hospital. The arrangement of rehabilitation programmes and the clinical outcomes of
patients are variable. The patients’ functional status in respective domain areas ties in
closely with the severity of the neurological insult and their entry level of functions
and the availability of social support at the outset (Tsoi 2004).
1.2.1.1 Impact of stroke
Burdens on stroke patients.
Dysfunctions of stroke can be multiple, chronic and
debilitating despite the fact that some patients find their functions readily restored
with minimal residual symptoms after the acute stage (Yu et al. 2003). For many,
6
stroke recovery can be complicated by different domain factors deriving from or
resulting in various impairments, co-morbidity and psychological and somatic
symptoms (Parikh et al. 1990, Morris et al. 1992). A study on quality of life (QoL),
with 85 consecutive patients (36 women and 49 men with a mean age of 65) with
first-ever ischemic stroke of a mild to moderate nature, showed that the overall QoL
of the participants was low (Kauhanen et al. 2000) and remained so for the first year
post-stroke (Kim et al. 1999, Suenkeler et al. 2002).
Burdens on families. Depression affects many stroke caregivers (41%), as reported
by Kotila et al. (1998). Socio-demographic, physical and psychosocial factors were
noted to be associated with depressive behaviour in family caregivers of stroke
survivors in the acute care phase (Grant et al. 2004). The presence of visible
impairments initially seemed to affect the spouses' emotional health, while cognitive
and emotional impairments among patients became more evident later in everyday
life. In the long term, the spouses' individual life situations and coping abilities are
expected to be more influential in moderating the well-being of the spouses
(Forsberg-Warleby et al. 2004).
Burdens on healthcare.
Depression following stroke can impede recovery and
prolong rehabilitation (Lai et al. 2002). Patients that are more depressed tend to need
longer hospital stays and have inefficient use of healthcare resources than those not
afflicted in that way (Schubert et al. 1992b, Gillen et al. 2001). This might be
associated with the low levels of self-rated health among stroke survivors, as
identified in current study results (data published already, see Publications). A
three-factor model with depressive symptomatology, physical disease and functional
disability may explain 79% of the variance in self-rated health among
community-dwelling older adults with stroke (Han et al. 2001). Alternatively, the
extended use of healthcare resources might also be indicated as residual impairment
can last beyond two years post-stroke (Parikh et al. 1988, Parikh et al. 1990) and
even up to seven years among patients with DAS (Dam 2001). The aggregate cost to
society of unattended DAS has been estimated to be high (Evers et al. 1997).
7
1.2.1.2 Interventions and rehabilitation for stroke survivors
The rehabilitation needs of stroke survivors are multifaceted and intensive
rehabilitation has therefore been advocated. Currently, more alternative interventions
are available to foster health-related quality of life despite the inevitable or irreversible
disabilities sustained by some stroke survivors. Three different kinds of rehabilitative
programmes or activities are found in the literature: (1) physical rehabilitation, (2) a
system approach to stroke rehabilitation, or (3) cognitive interventions.
A comprehensive approach has been indicated in the past decade which aimed at
tackling overt physical symptoms and covert ones that link with post-stroke
complications. Because of the overt functional compromise of many stroke survivors,
physiotherapy has been suggested, extending beyond the first year after stroke (Green
et al. 2004). The extension of physiotherapy to community settings such as old age
homes has also been initiated. This community physiotherapy programme or
home-based therapy allows flexibility in the onsite training of care providers and adds
to the convenience of older stroke people (Baskett et al. 1999). It was introduced to
patients with mobility problems one year after stroke. The study showed a significant
but clinically small initial improvement in the mobility and gait speed of users. But the
treatment outcome was not sustained after the programme ended (Baskett et al. 1999).
Another study was also unable to demonstrate the treatment effect on patients' daily or
social activity, anxiety, depression and number of falls, or on the emotional stress of
carers (Lord 2003). As much effort was invested to prevent and manage post-stroke
complications (McGuire & Harvey 1999), a self-directed learning module targeted at
healthcare workers caring for stroke patients which highlight the diagnosis and
treatment of co-morbidities and complications (Moroz et al. 2004) has been introduced,
and also adopted in Hong Kong. As another strategy to foster closer monitoring and
the provision of informational support to hospital and community healthcare providers
of stroke patients, telephone follow-up were tried. Telephone interviews have been
used prospectively in stroke studies to evaluate clinical outcome, functioning and
disability after stroke, cognitive status, stroke-free status and cognitive function in
community out-patients (Merino et al. 2005). The use of a single rater was suggested
to ensure the reliability of data collection, and this method was shown to be useful in
8
allowing more cost-effective monitoring of stroke rehabilitation progress. Along the
same lines, ‘tele-rehabilitation’ for community-dwelling stroke clients has been tried
in Hong Kong and seemed to be well received. The pilot study demonstrated the
feasibility, efficacy and high level of acceptance of tele-rehabilitation for
community-dwelling stroke clients (Lai et al. 2004).
Some previous studies have adopted a system approach for stroke interventions.
System approach in stroke care emphasizes an organizational rearrangement involving
corporate decision-makers and clinicians or healthcare providers at all levels. There
have been two commonly discussed modes of the system approach. One study has
compared the effects of an extended stroke unit service (ESUS) with the effects of an
ordinary stroke unit service (OSUS) on long-term QoL. But only marginal benefits on
QoL at 52 weeks compared with former service types were reported, and its routine
use was therefore not recommended (Fjaertoft et al. 2004). Another study worked on
the routine implementation of care pathways for acute stroke management or stroke
rehabilitation in hospital (Hankey et al. 2003). Most of the results were
non-randomised studies in which results might likely be influenced by potential biases
and confounding factors.
Cognitive interventions to improve post-stroke rehabilitation outcome or QoL
have been examined. The following are the proposed rationales for recommending
certain cognitive interventions recently:
1.
The rationale was that knowledge of strokes and clear expectations of
rehabilitation were important determinants of rehabilitation success. Thus
providing information on these two aspects and monitoring of such
psychological factors might help to promote positive outcomes (Clark &
Smith 1999).
2.
Potential use of cognitive therapy to reduce negative attitudes among
stroke survivors may be indicated, as it has been shown that short-term
parameters of stroke survivors predict worse long-term survival rates
(Lewis et al. 2001).
9
3.
Intervention to improve patient outcomes targeted on patients’ cognitive
power, such as perceptions of control six months after discharge, was used
to predict mortality three years after stroke. The outcome was significant
(Johnston et al. 2004).
1.2.2 Geriatric depression
Depression was defined as ‘the failure-to-thrive syndromes and represents a
psycho-physiological response to a chronic, seemingly unresolvable stress’ (Fred et al.
1997). However, the diagnosis of depression requires a clear definition and the use of
explicit assessment and diagnostic tools (Abram et al. 1995). The rising nation-wide
concern about depression has been noted (Commonwealth department of health and
aged care 2000; Dawson & Tylee 2001; European Commission-Health and Consumer
Protection Directorate-General 2005; Murray & Lopez 1996). The growing concern
about depression among the elderly was induced by the projected upsurge in
baby-boomers who will be 60+ in two decades’ time. Current healthcare supply is
unlikely to be able to meet the demand from this group, who may take up a large share
of medical provision (Lo & Yeung 2001). In addition, geriatric depression (GD) that is
likely to have an effect on patients’ perceived health may directly affect healthcare
outcomes (Krause et al. 1998) and the use of social and medical resources (Johnson et
al. 1992). Among patients with depressive symptoms, somatisation during medical
consultations frequently occurs (Woo et al. 1994, National Institute of Mental Health
2003), thus increasing healthcare utilisation such as outpatient visits (Glasser &
Gravdal 1997). This vicious circle may well aggravate the economic burden of serving
elderly people (Murray & Lopez 1996, Glasser & Gravdal 1997). National morbidity
statistics for England and Wales in 1981 showed that psychiatric disorders (9%) were
the third most common cause of primary care consultations, following those
concerning the respiratory (15%) and cardiovascular (11%) systems (Glasser &
Gravdal 1997). Estimates from the US suggest that among adults aged 18 or over in a
given year the prevalence of depression was about 9.5% (National Institute of Mental
Health 2003). A complex random sample in five US communities with 18,571 adults
in the Epidemiologic Catchment Area Study showed a life-time prevalence of
depressive symptoms of 23% (Narrow 1998).
10
The prevalence of depressive symptoms among older adults is widespread all
over the world (Illife et al. 1991, Woo et al. 1994, Liu et al. 1997, Lyness et al. 1997,
Lu et al. 1998, National Institute of Mental Health 2003). Justifications for more
healthcare concern towards geriatric depression include its chronicity (Pulska et al.
1999) and its association with mortality, in particular suicides (Yip et al. 1998, Pulska
et al. 1999). Epidemiological data over 12 countries revealed an association between
suicides and major depression among older adults (Conwell & Brent 1995). Similar
results were obtained in local and subsequence studies (De Leo & Diekstra 1990, Yip
et al. 1998, Denton 1999, Pulska et al. 1999, Davies et al. 2001). Other related studies
on geriatric depression have increased exponentially all over the globe in the past few
years. Some studies have examined the epidemiology of the GD (Mann 1992, Abram
et al. 1995, Nussbaum 1997, Gallo & Lebowitz 1999, Forsell 2000, Haynie et al. 2001).
These studies covered the predisposing factors (Krause & Liang 1992, Lyness et al.
1997, Leung 1998, Alexopoulos et al. 1999, Pulska et al. 1999, Badger &
Collins-Joyce 2000, Forsell 2000, Hwang et al. 2000), detection and assessment (Rapp
et al. 1988, Illife et al. 1991, American Psychiatric Association 1994b, Lam et al. 1995,
Glasser & Gravdal 1997, Lee et al. 2006a), screening tools development (Radloff 1977,
Brink et al. 1982, Chiu et al. 1994, Lee 1994, Lam et al. 1995, Boey 1999b), and
treatment and management guidelines of GD in primary care (Glasser & Gravdal 1997,
Scott et al. 2002, Gerstenlauer et al. 2003).
1.2.2.1 Assessment and prevalence of geriatric depression
Prevalence of GD among Chinese subjects. Geriatric depression is common in the
local territory and among Chinese people generally (Woo et al. 1994, Lam et al. 1995,
Boey & Chi 1998, Leung 1998, Lee 2003, Lam et al. 2004). The prevalence of GD
among Chinese subjects residing in Taiwan, Mainland China and Hong Kong was
estimated to range from 10% to 35% in community samples. For example, the
reported prevalence of GD among suburban subjects in Taiwan was 34.8%, using 11
out of 30 as the cut-off point in the Geriatric Depression Scale (Lu et al. 1998). But
that from rural samples in Taiwan reached only 26%, using 5 out of 15 as the cut-off
point in the Geriatric Depression Scale short form (Liu et al. 1997). The prevalence
of GD in Mainland China was found to be 18.2% for a random sample of residents
11
aged 60 or over in Beijing, Shanghai and Guangzhou in the mid 1990s (Song & Chi
2001). The Center of Epidemiologic Studies of Depression scale (CESD), with a
cut-off point of 16 or over indicating depression, was used in Song’s study. Typical
problems occurred in comparing results of these studies, which had adopted different
assessment tools or used different cut-off points with the same scale on Chinese
subjects who may be residing in different parts of Asia.
Prevalence of GD in Hong Kong. In Hong Kong, as far as community samples were
concerned, the prevalence estimate ranged between 10% (Lee 2003) and 35% (Woo
et al. 1994). One study reported an adjusted prevalence of geriatric depression to be
as high as 35% (29.2% for male subjects and 41.1% for female subjects) from a
stratified random sample of more than 1500 subjects, age 70+, receiving old age and
disability allowance (Woo et al. 1994). Another local study recruited subjects from
old-age homes (n=252) and old-age Centers (n=644), and yielded a lower GD
prevalence: 11% and 10% respectively (Leung 1998). Both studies used the Geriatric
Depression Scale short form (GDS-SF) as the screening tool. Eight or over was used
as the cut-off point indicating depression (see Table 1.1). Among more than 700,000
older citizens (aged 65 or over) in Hong Kong, less than 20% were receiving
disability allowance, and less than 10% were living in institutions (Social Welfare
Department 2003). Frequent morbidity among older adults is common (Lo & Yeung
2001), though they do not necessarily suffer from self-care deficits at the same time.
A recent cross-sectional study has estimated that 10% of 1,130 ambulant older adults
(60+) suffer from depressive symptoms as assessed by GDS-SF (Lee 2003) (see
Table 1.1). Such a figure is close to a previous local study recruiting members of
old-age Centers or homes in which the majority of residents can undertake self-care
and are mobile (Leung 1998).
12
Table 1.1 Prevalence of GD in Hong Kong
Leung, 1998
Woo et al, 1994
Lee, 2003
Convenience sample from
Stratified random of elderly
Convenience sample of
Elderly Home (EH) and Elderly
people receiving disability
Community dwelling older adults
Center (EC)
allowance
N= 1130 attendees
N= 252 (EH); N= 644 (EC)
N=1511
N=140 non-attendees
Age
age 60 or over
age 70 or over
age 60 or over
Screening
GDS-SF (cut-off point: >/=8)
GDS-SF (cut-off point: >/=8)
GDS-SF (cut-off point: >/=8)
11% (EH);
35%
10% (attendees)
Sample
Tool
Prevalence of
10% (EC)
GD
GDS-SF: Geriatric Depression Scale – short form
1.2.2.2 Factors associated with geriatric depression
Most studies have identified multi-faceted rather than unidimensional predictors
or risk factors for GD. Common risk factors may be categorised as follows: (1)
socio-demographic, (2) health status, (3) psychosocial support and (4) life events.
Socio-demographic factors.
Some studies have mentioned that GD tends to
increase with age (Woo et al. 1994, Blazer 1997, Kotila et al. 1998, Lu et al. 1998).
But such a relationship becomes insignificant when age is put into a regression
model together with physical impairments (Woo et al. 1994, Forsell 2000). Two
studies reported that males were more prone to GD (Woo et al. 1994, Lam et al.
1995), especially males in the old-old age group (80 years old or over). The
Department of Health in Hong Kong reported that men were 51% more likely to
commit suicide than women. Almost half of the male subjects reported that they had
felt depressed during the past 12 months (Department of Health 1999). Conversely,
some studies have also suggested that females are more prone to GD (Zunzunegui et
al. 1998, Harwood et al. 1999, Wang 2001), especially widows, as remarked in a
local report (Woo et al. 1994). Other less frequently mentioned demographic factors
include poor financial status (Woo et al. 1994, Krause et al. 1998, Chong et al. 2000).
In fact, Blazer has proposed a similar list of possible risk factors in different samples:
male over 55, painful or disabling physical illness, living alone or financial problems
and recent bereavement (Blazer et al. 2000).
13
Health status. Health status in terms of the number of co-morbidities (Kurlowicz
1997, Zunzunegui et al. 1998, Forsell 2000, Ormel & Von Korff 2000, Song & Chi
2001) or a history of psychiatric or mood problems (Pulska et al. 1999) were found
to correlate significantly with GD. Increased levels of dependency resulting from a
chronic disease were proposed as the root cause of GD (Femia et al. 1997, Badger &
Collins-Joyce 2000). One study has established the comorbidity of cognitive decline
with depression. 500 85-year-old people in the community were interviewed
annually for four years. The development of depressive symptoms co-existed with
cognitive impairment. On the other hand, the presence of depression alone might not
increase the risk of cognitive decline (Vinkers et al. 2004).
Psychosocial support and life events. The role of buffering factors for GD, such as
social support especially from children and spouse, were found to have a protective
effect against GD (Woo et al. 1994, Kurlowicz 1997, Bee et al. 1998, Krause et al.
1998, Lu et al. 1998, Denton 1999, Forsell 2000, Song & Chi 2001). Low emotional
support from children, lack of a confidant and few social activities were found to
relate to depressive symptoms identified by CESD among 1116 subjects (Zunzunegui
et al. 1998). Life events refers to the presence of current loss or bereavement (Forsell
2000, Hwang et al. 2000) or other unhappy episodes in life (Kurlowicz 1997). A local
study also looked into the effect of perceived autonomy among residents in elderly
homes, and found that the more autonomy the elderly had in daily chores, the less
prone they were to suffer from depression (Leung 1998). A sense of lack of control
were independently associated with high levels of depressive symptoms (Zunzunegui
et al. 1998).
1.2.2.3 Interventions for geriatric depression
Use of oral anti-depressants. A systematic review of the efficacy of anti-depressant
classes for the depressed elderly is available. A total of 32 trials provided data for
systematic analysis, and 17 studies contributed data towards the efficacy analysis.
These studies were carried out from 1986 to 2001 and most (12 out of 17) had a
sample size smaller than 50 in each or both of the arms. The paper reported tentative
evidence regarding efficacy as a change in the Hamilton Depression Rating Scales
14
(usually a reduction of 50% or a drop below a preset score). Secondary outcome
included the total withdrawal rates associated with each class and the description of
side-effect profiles as the third study outcome (Mottram 2006). The inclusion criteria
for the review were:
1. Randomised control trials comparing at least two oral anti-depressants;
2. Depressed older adults, aged 55+;
3. Settings for subject recruitment: in-patients, outpatients or communities;
4. Anti-depressants groups: these were grouped according to the British
National Formulary (British Medical Association 1999) as follows, with
some examples:
z
Classical tricyclic anti-depressants: amitriptyline, clomipramine,
desipramine,
dothiepin,
doxepin,
imipramine,
nortriptyline,
nomifensine, trimipramine
z
Tricyclic-related anti-depressants: maprotiline, mianserin, trazodone
z
Selective serotonin e-uptake inhibitors (SSRI): paroxetine, fluoxetine,
citalopram, fluvoxamine, sertraline
z
Atypical anti-depressants (all newer anti-depressants that do not fall
into this list):
z
buspirone, venlafaxine, reboxetine
z
monoamine inhibitors (MAOI): phenlzine, moclobemide
The main findings indicated that both classical and related tricyclic
anti-depressants were equally efficacious when compared with SSRIs. In terms of
withdrawal rates due to side effects, classical tricyclic anti-depressants were
significantly higher compared with SSRIs but not the tricyclic-related anti-depressants.
The authors advised caution in the use of their results as the relatively small number of
studies (when examined by anti-depressant group) restricted the nature and validity of
conducting sub-group analysis on these differing populations, including different
types of depression. In examining comparative efficacy data, findings have been
limited through small sample sizes and poverty of data, thus potentially limiting the
implications of the findings (Mottram 2006).
15
Use of non-pharmacological interventions for depression. Besides oral medication,
different interventions have been introduced to treat or alleviate depressive
symptoms among older adults. Exercise and psychotherapy are two of the most
researched interventions. 1,801 primary care patients aged 60 or more with major
depression, dysthymia or both were randomly assigned either to a 12-month
collaborative care intervention or to standard care for depression. Significant effects
were observed between intervention and control groups (Hunkeler 2006).
A nurse-led programme aimed to pilot the effect of social interaction sessions
delivered by volunteers with residents in old-age homes after in-depth depression and
psychosocial assessment. The social interaction sessions were held twice weekly for a
12-session programme for volunteers, and an evaluation of the intervention outcome
in a RCT study. A significant reduction in depression was reported between the
treatment and control groups (McCurren et al. 1999).
Using exercise to reduce depression seems promising though concrete evidence
is not available because of poor data (Lawlor & Hopker 2001). A later study on 2,000
older adults claimed that physical activity had a protective effect against depression
(Strawbridge et al. 2002).
1.2.3 Depression after stroke
Despite suspicions of frequent psychological comorbidity among stroke
survivors, formal studies on depression after stroke were not conducted till the early
80s (Feibel & Springer 1982, Robinson et al. 1985, House 1987, Primeau 1988). The
research was directed towards testing hypotheses and explaining the causation of DAS
after formal identification of prevalence or incidence of DAS in various populations. A
detailed discussion of the available studies to test various hypotheses will be offered in
a forthcoming section.
1.2.3.1 Impact of DAS
Researchers have made it explicit that one of the compelling reasons for
16
studying DAS is the burden it imposes on multiple parties (Robinson 1997). Multiple
comorbidities among DAS victims have been reported. Various functional losses are
found (Johnston et al. 2004, Muslumanoglu et al. 2004), in particular after discharge
(Nannetti et al. 2005). Depression is a frequent and clinically relevant sequela in
patients who have suffered from an attack of single or repeated cerebrovascular
lesions. Despite the contribution of 20 years’ study, there are still too many questions
and too few answers regarding the pathophyisology of DAS. The challenge
originated from the variations in diagnostic approach and nosographic status, as well
as the complex nature of multifaceted disease etiology and the diversity of clinical
outcomes that depression, stroke or DAS may individually bring forth (Provinciali &
Coccia 2002).
The chronicity of DAS adds a further burden as it persists beyond the first year
once detected (Catapano & Galderisi 1990, Everson et al. 1998, Britta et al. 1999).
Depression has been reported as the strongest factor associated with the Philadephia
Geriatric Center Morale score among a group of stroke survivors three years post
stroke (Britta et al. 1999). Later study also identified DAS as one of the predictors of
quality of life (Carod-Artal et al. 2000).
Quality of life (QoL) was shown to be the most affected among patients with
emotional complications besides stroke or chronic pain (Frühwald et al. 2001). Results
showed a negative impact of DAS on the functional recovery process after discharge,
which had not been the case during hospitalization (Nannetti et al. 2005). Only two
studies disputed the effect of DAS on rehabilitation parameters (Miller 2000, Johnston
et al. 2004). Comorbidity of DAS was also reported in 47.5% of major depressed
stroke patients who had anxiety disorder at the same time. And of those with anxiety
disorder, 27.5% suffered major depression (Beekman et al. 2000). DAS was associated
with higher mortality (Everson et al. 1998, Gupta et al. 2002, Williams et al. 2004)
even after adjustment for factors associated with stroke severity at 12 or 24 months
(House et al. 2001). One in 11 patients (26/300) seen at the general neurology clinics
had given serious thought to committing suicide in the past two weeks. Almost all of
these patients (23/26) had major depression (Carson et al. 2000a).
17
1.2.3.2 Incidence and prevalence of DAS
There have been studies recruiting national samples (Burvill et al. 1995,
Beekman et al. 1998, Weimar et al. 2002, Eriksson et al. 2004). The Perth Community
Stroke Study (PCSS) was population-based and examined the incidence, causes and
outcomes of acute stroke. Among 294 patients, the incidence of depressive illness four
months after stroke was 23% (18-28%), 15% (11-19%) major depression and 8%
(5-11%) minor depression (Burvill et al. 1995). Using a case-control study design in
another large community-based study of 3,050 older people (aged 55-85) in three
regions of the Netherlands, the prevalence of depression in survivors of stroke,
measured by the Center for Epidemiological Studies Depression Scale (CESD), was
estimated to be 27%, significantly higher than the base rate (OR 2.28, 95% CI
1.61-3.24) (Beekman et al. 1998). In Germany, 67% of 4,264 stroke survivors were
followed up at 100 days and one year post-stroke. The incidence of DAS assessed by
CESD with a cut-off point of 10 was estimated to be 32.9% (Weimar et al. 2002). A
Swedish national sample of 15,747 stroke survivors were asked about depressive
mood and anti-depressant treatment 3 months after stroke via the telephone. 12.4% of
male and 16.4% of female stroke survivors reported that they always or often felt
depressed. The variable self-reported depression (yes/no) was the primary outcome
variable in this study (Eriksson et al. 2004). Among rural Taiwanese, a study has
compared depression among 45 stroke patients with older adults in general as control
(n=1,471). The prevalence of depression using 15-item GDS with a cut-off point of 5
was 62.2% and 33.4% respectively (Fuh et al. 1997). Among Chinese samples (in
Hong Kong), DAS was estimated at between 16.4% (Tang et al. 2005) and 24.0% (data
already published; see Publications) in two separate studies using DSM IV. In fact, the
range of incidence or prevalence estimated in different countries can vary quite widely,
depending on the tools and the cut-off point chosen for decision-making. The
American Heart Association has stated that some 11-68% of stroke survivors suffer
depression, of whom about a third suffer major depression (Kelly-Hayes et al. 1998).
The temporal relationship between stroke and onset of DAS has been studied.
Studies generally argue that depression is very common in the first year after stroke.
The pattern and causes of depression onset among stroke victims at the early phase
18
(1-6 months) may differ from what occurs at later time (>1 years) in terms of
vegetative state and psychological functions. The findings have implications for
depression recognition and assessment (Tateno et al. 2002). Studies undertaken at
different time points are presented chronologically below:
Admission – 2 weeks.
An Irish sample showed that 10 out of 50 participants (20%)
met criteria for major depressive disorder on admission or within seven days
(Cassidy et al. 2004). In another pool of stroke survivors, aged 27-70, depressive
symptoms were relatively common two weeks after the first clinically significant
stroke (27% Beck Depression Inventory >/=10). The prevalence of major depression
was only 5.6% (Berg et al. 2001). But early onset of depression (ie immediately after
stroke, in the first or second week) has been regarded as a normal reactive mood that
is secondary to the sudden onset of a debilitating disease rather than having any
endogenous origin (Jones et al. 2000, Nys et al. 2005).
Hospital discharge - one year. Fifty-two patients in a rehabilitation Center were
assessed for depression using the Hamilton Rating Scale for Depression on
admission to and discharge from the rehabilitation Center. Upon discharge,
depression estimated for the first-ever stroke patients was 28.6% (Loong et al. 1995),
and it was noted that patients’ moods did not correlate with degrees of functional
impairment as reflected by scores on Barthel Activities of Daily Living (Loong et al.
1995). A study was undertaken at three months after stroke to assess the stability of
depressive symptoms. It showed that 46% (n = 23) of the total sample described
fewer depressive symptoms; while 44% (n = 22) indicated more depressive
symptoms, and 10% (n = 5) showed no difference from the depression scores at the
tenth day from discharge (Popovich et al. 2002). In another study at 3 months, 41%
of 297 first-ever stroke survivors in Bangladesh were identified as depressed on the
Beck Depression Inventory (BDI) against 18% of the control group (p <0.001). The
rate did not decrease at the one-year follow-up (42% versus 19% at one year, p<0.05)
(Hayee et al. 2001). A further study also supported the finding that DAS could be a
stable and chronic phenomenon from inception (Carson et al. 2000b).
19
Beyond one year. In studies undertaken after a year or more, the prevalence did not
seem to decrease significantly. Berg et al. (2003) reported 54% of all participants
studied felt at least mildly depressed at some time during the follow-up; 46% of
those who were depressed during the first two months were also depressed at 12 or
18 months. Another 12% or so were depressed for the first time at 12 or 18 months
(Berg et al. 2003). Another study looking at the prevalence of DAS at 3 years,
estimating about 34% (62 out of 180 participants) were depressed using a score of 14
on the BDI as a cut-off point (Angeleri et al. 1997).
Despite the above evidence on DAS incidence and prevalence, counter arguments
were also presented. Burvill and colleagues (1997) argued that DAS is no more
common than ‘ordinary’ depression in older people. Another study revealed that
depression occurred equally often during the first year after stroke and after
myocardial infarction when non-specific factors such as sex, age and level of handicap
were taken into account. Thus the relatively high incidence of post-stroke depression
seemed not to reflect a specific pathogenic mechanism (Aben et al. 2003). Another
study used a structural-equation modelling approach to examine depression symptom
endorsement between geriatric stroke patients and other geriatric patients. Results
indicated that stroke patients did not suffer more severe depression (Mast 2004).
1.2.3.3 Risk factors and predictors of DAS
To allow a systematic analysis over multiple interwoven factors, the domains
used in this thesis are grossly categorised into bio-anatomical domain, cognitive and
communicative domain, dependency and somatic domain, emotional domain or family
and social support domain. The inclusion of this information is indicated to form a
baseline reference for the comparison of impact resulting from DAS as identified by
this study. The hypotheses of DAS causation are closely related to the five domain
factors. The conceptual framework or schematic view of the respective domains of
risk factors or predictors for DAS is given in Table 1.2 below.
20
Table 1.2 Conceptual framework in postulating DAS causality
Endogenous Model
Functional Model
Relationship Model
Bio-anatomical domain
Cognitive and communicative
Emotional domain
domain
y Anatomical hypothesis
y Vascular hypothesis
y Biochemical hypothesis
Dependency and somatic symptom
Family and social support domain
domain
y It relates to innate and fixed
y It relates to the compromised
y It relates to human factors,
clinical states, relatively less
functions and tasks as an
both the stroke victims and
manipulations can be made
immediate consequences of
those with a relation with
once stroke outbreak for the
stroke; highly modifiable but
him/her;
anatomical hypothesis, but
still subjected to clinical states
more interventions are
resulted from stroke deficit
y These domain factors are
possible for vascular
modifiable and not
hypothesis and biochemical
necessarily tied with previous
hypothesis
models theoretically
Arguments:
Arguments:
Arguments:
This biological changes in brain
Dysfunctions and somatic symptom
The worries and anxiety resulted
resulted from stroke lead to DAS
after stroke lead to DAS directly
from the sudden stroke; lack of
directly
buffering factors (family and social
support), depriving stroke patients’
coping resources, lead to DAS
directly
Bio-anatomical domain. Scientists have attempted to look for specific evidence
outside the clinical association to explain a pathophysiological mechanism behind
DAS. Such findings have important implications for assessment and treatment
modalities (Hinkle 1998). Under the bio-anatomical domain, three different
hypotheses can be advanced for the explanation of DAS: anatomical, vascular and
biochemical.
Within the anatomical hypothesis, the lesions’ location and laterality, brain-area
involvement and size of lesions were studied (Robinson et al. 1990, MacHale et al.
1998, Berg et al. 2001, Rao 2001, Spalletta et al. 2003, Vataja et al. 2004). Robinson
and Starkstein have proposed that the side of brain lesion may impact on DAS, and
also the involvement of cortical or subcortical lesions (Robinson & Starkstein 1990).
Nelson (1994) suggested that patients with left-sided lesion showed a slower recovery
at 2 months after stroke, whereas emotional functions worsened mainly for those with
right-sided lesions at 6 months (Nelson et al. 1994). Harrington and Salloway (1997)
has suggested that anterior subcortical lesion will increase the risk of DAS. But the
21
relationship between laterality and DAS is not duplicated by other researchers
(Angeleri et al. 1993, Agrell & Dehlin 1994, Angeleri et al. 1997). Also, a recent
systematic review by Carson and his team did not support the effect of the location of
the brain lesion on DAS (Carson et al. 2000b). A later study then identified that
depression was associated with more severe stroke, particularly in vascular territories
that supply limbic structures (Desmond et al. 2003). Another study claimed that
subcortical and anterior cerebral artery (ACA) lesions were independent risk factors
for poststroke depression (Tang et al. 2005). On the other hand, Aybek (2005) has
refuted any association between acute emotional behaviour after stroke and
neurological impairment or lesion localisation.
Another subcategory in the bio-anatomical domain is the vascular hypothesis.
Dieguez et al. argued that post-stroke depression was a vascular depression (Dieguez
et al. 2004). To test this hypothesis, Mast et al. (2004) compared three groups of
patients to inspect the relationship between cerebrovascular risk factors and
depression. Data from 670 geriatric rehabilitation patients were incorporated to
compare the frequency of depression in three patient groups: (1) those with no
evidence of vascular disease or stroke, (2) those with cerebrovascular risk factors but
no evidence of stroke, and (3) patients with stroke. His team identified a significant
relationship between the second group of patients and frequency of depression (Mast
et al. 2004). A more recent study on the effect of haemodynamic factors on depression
in late life suggested that the observed reduction in cerebral blood flow velocity
measured by Transcranial Doppler Ultrasound could be the result of reduced demand
in more seriously depressed cases as a DSM-IV disorder, whereas lowered
carbon-dioxide induced cerebral vasomotor reactivity is a possible causal factor of
sub-threshold depressive disorder (Tiemeier et al. 2002). Further study also supported
this hypothesis, that cerebrovascular reactivity was significantly reduced in depressed
patients (Neu et al. 2004). However, whether the reduced cerebrovascular reactivity
among depressed stroke patients is a cause or an effect remains largely unknown and
requires further study.
The third sub-category of the bio-anatomical domains involves the biochemical
nature of depression, in particular the serontonin activity. Mayberg (1991) has
22
identified some temporal changes in cortical selective serotonin re-uptake inhibitors
(SSRIs) receptors post-stroke. There was only compensatory up-regulation of 5HT2
receptors in the right hemisphere but not on the left side (Mayberg et al. 1991). The
results differed from an earlier study, where no difference in platelet 5-HT uptake was
found across depressed and non-depressed subjects six months after stroke (Barry et al.
1990). But available evidence shows that post-stroke pathological crying is induced by
partial destruction of the serotonergic raphe nuclei in the brainstem or their ascending
projections to the hemispheres (Andersen 1997). Mayberg attempted to explain a
biochemical origin of depression causation in two possible pathways, as evidenced by
a set of post-treatment imaging results that signaled different neurological pathways of
depressed subjects using cognitive behaviour therapy (CBT) or oral anti-depressants
respectively. He added, ‘Anti-depressant drugs change the neuro-chemical balance in
the brain through effects at specific target sites. Cognitive behaviour therapy also
changes brain activity: it's just tapping into perhaps a different functional component
of the depression circuit board.’ (Quoted in Mayor 2004, pp 7431).
Cognitive and communicative domain.
Occurrence of dementia among stroke
patients is frequent. DAS was shown to correlate with both physical and cognitive
impairment (Robinson 1997). Cognitive decline was found among 61.7% of stroke
patients (Pohjasvaara et al. 1997), and was significant as a factor associated with
depression in another study (Kauhanen et al. 1999). There was a concern that
overlapping mood and cognitive symptoms made detection less explicit (Bourgeois
et al. 2004). Interestingly, the association between depression and cognitive
impairment was reported to occur primarily in patients with left-hemisphere lesions,
and was strongest during the acute post-stroke period (Kurz 1997). Dementia was
associated with female gender, age and lower education level (Madureira et al. 2001).
Abnormal illness behaviour was also reported among some stroke survivors (Clark &
Smith 1998).
Like depression, DAS has been explained by cognitive theorists as behavioural
sequela of irrationality and errors in one’s thinking process. This argument has some
support in that significantly more negative cognitions and fewer positive cognitions
were found among stroke patients who were depressed than among stroke patients
23
who were not (Nicholl et al. 2002).
Despite such evidence, the cognitive depression hypothesis was refuted in a study
showing that patients with major post-stroke depression whose mood improved at
follow-up had significantly greater recovery in cognitive functioning than patients
whose mood did not improve, suggesting that major post-stroke depression leads to
cognitive impairment and not vice versa (Murata et al. 2000). Another earlier study
examining cognitive function among a cohort of 74 stroke subjects in the Framingham
Study also found that post-stroke patients suffered more depression as assessed by
CESD, but their intellectual decline appeared to be independent of the presence of
depression (Kase et al. 1998).
Dependency and somatic symptoms domain. Many studies have reported the effect
of post-stroke disabilities, body discomforts and self-care dependence on depressive
symptoms (Fuh et al. 1997, Beekman et al. 1998, Paradiso & Robinson 1998, Kao
2000, Lalasingh-Nixon 2001, Gainotti & Marra 2002, Weimar et al. 2002, Eriksson
et al. 2004, Johnston 2004, Muslumanoglu et al. 2004, Nannetti et al. 2005). Studies
advocating the reactive depression hypothesis argued that DAS was primarily a
normal reactive response to the loss of body functions and stress arising from
increasing dependency at about one month after stroke onset, rather than of
endogenous origin (Jones et al. 2000), in particular the earlier phase of stroke (Nys et
al. 2005). DAS was significantly associated with the level of disability recorded by the
Functional Independence Measure (FIM), and level of handicap recorded by the
Nottingham Extended ADL Index (NEADLI) and left-hemisphere stroke, at four time
points in the first year (3, 6, 9, 12 months) (Gottlieb et al. 2002). Kao (2003) found that
patients' functional ability was the strongest predictor for DAS among Taiwanese.
Several stroke-induced complications are well documented. Neuropathic or
central pain has been estimated to occur in up to 8% of patients after a stroke.
Functional disturbances such as depression, anxiety and sleep disturbances are
significant comorbid conditions associated with central post-stroke pain (Hansson
2004). This is a further example showing comorbidity of emotional domain factors
on physical somatic domain factors. Constipation is recognised as a serious problem
24
in clinical practice, affecting 60% of those in stroke rehabilitation wards (Robain et
al. 2002). But a single educational intervention has effectively improved symptoms
of bowel dysfunction up to 6 months later, changing bowel-modifying lifestyle
behaviours up to 12 months later (Harari et al. 2004). Stroke patients are notoriously
susceptible to fall episodes (Jorgensen 2002). Among the stroke patients, depressive
symptomatology
predicted
falls,
which
are
more
frequent
among
non-institutionalised long-term stroke survivors than among community control
subjects (Jorgensen et al. 2002). This forms a useful clinical example to demonstrate
the temporal effect of depression on falls, though the reciprocal occurrence can also
take place. It illustrates the complexity of studying DAS when factors in different
domains may interact in two directions.
Straightforward as it is, the dependency level of stroke survivors may also be
affected by the confounding effect of family functioning and somatic problems. One
study was able to identify family functioning as a significant factor directly
influencing lifestyle activities and self-care performance rather than the patient’s
ability to carry out self-care functions at six and twelve months (Clark & Smith 1999).
Other noxious somatic symptoms may also have an impact on self-care functions.
Thus guidelines for caring for older community dwellers reiterate the need to ensure
the adequacy of pain control in patients with chronic pain, and to assess its relationship
to depression (Gerstenlauer et al. 2003). It has been suggested that sub-threshold
depression is co-morbidity with somatic illnesses and physical disability (Geiselmann
et al. 2001). Pre-morbid somatic complaints are common in old age. The fact that there
is a reciprocal relationship between somatic symptoms and DAS has complicated the
DAS causative model.
Emotional domain. This area has earned more attention over the past decade. Some
argue that the effect of the emotional domain on stroke survivors may have superseded
the other domains, and are supported by some studies (Jonkman et al. 1998, Kim et al.
1999, Kauhanen et al. 2000, Perry & McLaren 2004). Among various mood exhibits,
depression was most prevalent, with anxiety disorder, fatigue and psychosis less
common among stroke survivors (Bourgeois et al. 2004). The degree of depression on
admission was a good predictor of the clinical outcome of the final physical
25
impairment (Loong et al. 1995). Emotional health, independent of other baseline
measures, is associated with recovery in functional ability one year after stroke
(Chemerinski et al. 2001). The findings suggest that reducing pre-morbid levels of
depressive symptoms (during initial stroke admission) or increasing positive affect
may help the recovery process (Chemerinski et al. 2001, Ostir et al. 2002).
Additionally, in a case-control study with 290 stroke inpatients, non-depressed
patients were nearly twice as likely to show an excellent recovery rate in both self-care
and mobility as the depressed patients (Paolucci et al. 2001; Parikh et al. 1990). One
study has suggested that about 10% post-stroke patients fulfilled criteria for
post-traumatic stress disorder (PTSD) (Sembi et al. 1998). Other findings have
reported the development of suicidality among stroke survivors (Kishi et al. 1996,
Kishi et al. 2001, Pohjasvaara et al. 2001), thus pointing to direct and indirect causes
of mortality (Robinson 1997, Everson et al. 1998).
Family and social support domain. The importance of social support in reducing the
neuro-behavioural consequences of stroke has also been discussed over the last decade
(Swartzman et al. 1998). The most significant predictor for social functions after
stroke in Angeleri’s study (1997) was depression. Multiple regression analysis from
another study revealed that ‘worrying about disease’, ‘worrying about family’ and
informational support from family, relatives and friends accounted for 22.1% of the
variance in depression (Li et al. 2003). The role of family support has been regarded as
a buffering factor against suffering and might enhance the quality of life of stroke
survivors (Swartzman et al. 1998, Kim et al. 1999, Jaracz & Kozubski 2003, Tang et al.
2005). Results from 87 survivors indicated that larger social networks were associated
with fewer limitations in physical function and a lower risk of institutionalisation
(p<0.05), controlling for relevant health and socio-demographic conditions
(Cohen-Mansfield & Taylor 1998).
The discussion over the role of social support in relation to DAS at different time
points was less explicit. Some authors proposed that psychosocial factors were more
important in the medium term (from six months to two years after stroke) (Astrom et al.
1993). But Robinson has remarked that social support is particularly important in the
first few weeks, as are material needs such as finance following the acute phase
26
(Robinson et al. 1999).
Recently the families or care-givers of stroke survivors have also reported
compromised QoL due to an overwhelming care-giving burden (Angeleri et al. 1993,
Anderson et al. 1995, Martin et al. 1998, Chumbler et al. 2004). Increasing evidence
has shown the frequency of depression among carers of stroke patients (Tompkins et
al. 1988, Angeleri et al. 1993, Dennis et al. 1998) in particular when patients have
been dependent before stroke (Dennis et al. 1998). This information signifies a
deeper understanding regarding the dynamics and hidden agenda in a possible
reciprocal relationship between social and family support and DAS in stroke patients.
It is hoped that this study may offer some insights into the role of social support
domain factors on DAS.
Demographic and other factors. Age was one of the most common confounding
factors on DAS causality, with the older age groups having a higher risk (Berg et al.
2001; Fuh et al. 1997; Kao 2000). While others found no such relationship (Gesztelyi
et al. 1999, Nys et al. 2005) or proposed that ages younger than 65 would be a risk
factor (Beekman et al. 2000, Eriksson et al. 2004).
Regarding gender and its relationship to DAS, females have been reported more
susceptible, and such a view predominates in the literature (Cassidy et al. 2004 ;
Desmond et al. 2003; Eriksson et al. 2004; Miller 2000; Paradiso & Robinson 1998;
Tang et al. 2005; Weimar et al. 2002), except in one study (Berg et al. 2003) which
suggests the opposite. One study found that women with major depression had a
greater frequency of left-hemisphere lesions than men. Major depression among men
was associated with greater impairment in daily living activities (Paradiso & Robinson
1998). Others did not identify any gender effect on DAS (Burvill et al. 1995, Nys et al.
2005).
27
1.3 Indications for current study
1.3.1 Gaps in previous studies
Taking a stance from the above arguments, research into the incidence and
predictors as well as the interventions of DAS is indicated. Undue attention to help
DAS victims, who are likely to be suffering from a triple burden, is ethically
unacceptable. But there are two major unanswered questions to be considered before
addressing the condition in the treatment paradigm. The prevalence of DAS among
Chinese needs to be quantified. If DAS is a common phenomenon, then we need to
study the risk factors and the predictors of DAS. The present study has been initiated
against such a background and rationale. And related research is not readily available
in Chinese populations at the commencement of this research.
1.3.2 Research questions
The research questions of the current study are as follows:
z
What is the incidence of DAS among Chinese subjects who have ischemic
stroke for the first time?
z
What are the risk factors and predictors for DAS?
z
What are the changes in various domains factors among stroke survivors
during the acute phase (1 month) and after active rehabilitation (6
months)?
z
What is the clinical outcome of the depressed stroke survivors at one
month and six months?
z
In line with the literature, what are the recommended interventions for
DAS?
28
Chapter 2 Methods
2.1
Aim and objectives of study (31)
2.2
Study design (32)
2.2.1 Justification of study design (32)
2.2.2 Ethics approval and consent (33)
2.2.3 Study procedures (34)
2.3
2.2.3.1
Record reviews (34)
2.2.3.2
Interviews (35)
Sampling (37)
2.3.1 Target populations (37)
2.3.1.1
Inclusion criteria (38)
2.3.1.2
Exclusion criteria (39)
2.3.2 Sampling method (39)
2.3.3 Sampling size (39)
2.4
Data collection (41)
2.4.1 Study outcomes - the incidence and predictors of DAS (41)
2.4.1.1
Tools for diagnosing and quantification of depression in
patients suspected of DAS (41)
y DSM IV
y GDS
y CESD
2.4.1.2
Use of smiley diagrams to detect depression in patients suspected
of DAS (44)
2.4.1.3
Health literacy regarding depression (45)
2.4.1.4
The domain factors associated with DAS and predictors of DAS
(46)
y Bio-anatomical domain
y Cognitive and communicative domain
y Dependency and somatic symptom domain
y Emotional domain
y Family and social support domain
y Socio-demographics and other clinical sequelae
29
2.4.2 DAS interventions (50)
2.4.3 Pilot study (50)
2.5
Data analysis (53)
2.5.1 Data management (53)
2.5.2 Patients’ demographic and health profiles (53)
2.5.3 Validity of the assessment tools (53)
2.5.4 Incidence of DAS at one month and six months (54)
2.5.5 Risk factors and predictors of DAS (54)
2.5.6 Changes of domain factors from one month to six months (56)
2.5.7 The clinical outcomes of stroke survivors (56)
2.5.8 Qualitative analysis – depression literacy (57)
30
2.1 Aim and objectives of study
This study was aimed at identifying the incidence of depression after stroke
(DAS), the risk factors and predictive factors at one month and six months after stroke
onset. Basing itself on the findings of the primary outcome, the study continued to
address the secondary outcomes in terms of detection of DAS, health literacy
regarding depression among Chinese stroke patients and interventions in DAS.
The study objectives were:
1.
To estimate the incidence of depression among Chinese patients with
first-ever ischemic stroke attack at one month and six months;
2.
To identify the domain factors that are associated with and independently
explain DAS at one month and six months;
3.
To understand the changes in the five proposed domain factors: the level
and significance of changes in the case of DAS;
4.
To identify the factors at one month that can predict DAS at six months;
5.
To examine health literacy in respect of depression among stroke
survivors;
6.
To
explore
clinical
outcomes
(use
of
formal
healthcare
and
anti-depressants, institutionalisation, mortality, quality of life) in stroke
survivors;
7.
To discuss possible DAS interventions and implications for future research,
practice and education.
31
2.2 Study design
This study adopted a descriptive longitudinal study design. Longitudinal study is
a non-experimental design that compares one group’s status at two or more points in
time (Polit & Beck 2004). It is also referred to as panel studies, which attempt to
identify the causal role of exposure to a risk factor in generating changes in health or
causing disease (Strommel & Wills 2004). The researcher does not play an active role
in the manipulation of the cause and effect relationship in an occurrence of a topic of
interest. It is considered most appropriate if the phenomenon studied is either known
or hypothesised to change over time. This helps to provide important information in
the study of disease progressions such as the changes in quality of life, physical
functioning and mental health of patients with particular chronic diseases (Kurtz et al.
2001, Strommel & Wills 2004). Thus exposure to ischemic stroke is considered an
illness-causing agent or risk factor in respect of depression. A major merit of such
designs is that they fulfil one major criterion for causality, and multiple clinical
outcomes or potential consequences can be examined at the same time (Nurses Health
Study 2002). Unlike a cohort study design, longitudinal studies do not have a control
group. An inception cohort of depression-free first-ever ischemic stroke subjects was
recruited for the present study. The medical records of participants were reviewed after
obtaining their written consent and upon discharge to ensure that genuine stroke
participants had been recruited and to facilitate the checking against the admission
criteria of the study. This strategy helped to maintain the quality of the depression-free
and stroke-free cohort, thus allowing a true incidence rate of DAS to be obtained.
2.2.1 Justification of study design
A longitudinal study design was one of the commonest types occurring in the
literature to identify the incidence of various disease and stroke conditions (Parikh et
al. 1988, Bacher et al. 1990, Astrom et al. 1993, Colantonio et al. 1993, Nelson et al.
1994, Femia et al. 1997, Clark & Smith 1999, Singh et al. 2000, King et al. 2001, Aben
et al. 2003, Forsberg-Warleby et al. 2004, Jorge & Robinson 2004, Toso et al. 2004,
Whyte et al. 2004). It had also been used by national studies of DAS (Jonas &
Mussolino 2000, Toso et al. 2004). The reasons for not incorporating a control group
32
were multiple:
1.
Study objectives - There was no existing good quality study addressing
DAS in Chinese speaking communities when our study was first launched
in Hong Kong. A longitudinal study design was adequate to set the stage in
understanding the occurrence, natural disease progression and predictors of
DAS in Chinese, which were largely unknown.
2.
Resources implications - Capturing a group of comparable older people
who were free from cardiovascular risks including hypertension, diabetes
and ischemic heart disease would involve prohibitive human and financial
resources.
3.
It was a logistic challenge to engage elderly stroke patients who were
likely debilitated and suffering might make them unwilling/unable to talk
with researchers. This was reflected in the fact that three hired research
assistants were unable to obtain valid consent from the targeted number of
first-ever ischemic patients (n=30) within three months during the pilot
phase, signifying the challenge and practical difficulty for less experienced
research staff.
4.
The well established hierarchy and bureaucracy of Hong Kong hospital
structure might not allow a researcher with nursing background working
outside the hospital to carry out a study with a complex design involving
multiple units in the hospital. This was evidenced by overnight breach of
agreement to collaborate after six-months’ negotiation and ground work
preparation in some hospitals.
2.2.2 Ethics approval and consent
The study protocol sought ethics approval from the Institutional Review Board of
the University of Hong Kong and the two regional hospitals to access patients’ medical
records. The schedule of the research project was included in the study protocol to
guide the momentum of the study (Appendix 2.1). All patients admitted to the stroke
units of the two hospitals with a provisional diagnosis of stroke were screened for
inclusion. Only those who fulfilled the preset inclusion and exclusion criteria were
contacted in person or by phone for their consent. After an explanation of the study’s
33
purposes and its procedures, assurance would be provided to potential subjects
regarding any influence on standard care and confidentiality of personal data.
Participants were given time to clarify their queries before signing the consent form
and one copy of the written consent was issued to the patient, one filed in his/her own
medical records and the original kept by the researcher. Essential information such as
the name and contact number of the researcher was given in the consent form
(Appendix 2.2). Subjects were reassured of the right to withdraw from the study at any
time without any influence on current medical and healthcare so as to ensure voluntary
participations. During the consent stage, one of the nursing staff working at the stroke
unit would be a witness and sign to verify suitable case selection. At times, patients’
significant others were also briefed about the purpose and arrangement of the study if
indicated.
2.2.3 Study procedures
Consecutive patients admitted to the stroke units of the two regional hospitals
were screened with reference to their provisional diagnosis on admission for
recruitment eligibility. The case identification phase fell between 1 June 2004 and 31
May 2005. The subjects who fulfilled both inclusion and exclusion criteria and gave
their consent to participation in the study would form the inception cohort.
2.2.3.1 Record reviews
With the approval of the respective hospital, the medical records of the
consenting participants were studied at three different time points: (1) after obtaining
consent to ensure adherence to inclusion and exclusion criteria and vetting the past
medical history to rule out mental health history or stroke, and retrieving
socio-demographic data; (2) after first interview at one month; and (3) following the
second interview after six months. One of the most important indications for the first
two record reviews was to screen out those with a documented medical history of
depression and stroke. Where queries over the diagnosis or other ambiguity existed,
the consultant neurologist’s final verification would be sought. The third review was
conducted to capture qualitative data relating to depression assessment and treatment,
34
to supplement information during the natural course of disease progression from one
to six months, such as concurrent life events or use of psychoactive drugs (see section
2.4.1.4). Additional record reviews were sometimes indicated if there was ambiguity
over patients’ data, or missing laboratory or investigation results that were scheduled
after hospital discharge.
2.2.3.2 Interviews
Personal interview was one of the most frequent methods used for studying DAS,
in particular among older subjects in Hong Kong. The reasons were as follows:
Foster participation. Older Chinese adults, especially those depressed, might not be
motivated to join any study revealing their ‘inner’ emotions as it is uncommon in this
culture to readily disclose one’s emotions to strangers, particularly in those of the older
generation. A face-to-face interview by a person showing empathy and comforting
attitude was therefore needed to establish rapport first and then solicit sensitive and
sentimental data.
Low literacy in respect of depression. The stroke subjects were mostly older adults
born after World War II and immigrants from the countryside in China with no or little
formal education (Butters 1939, Endacott 1964, Tsang 2004). They seldom hear
words like ‘depression, hope, hobbies’ and thus would have difficulty understanding
the questions addressing symptoms of a major depression. Another barrier was that
some older adults were reluctant or unable to disclose emotional pain masked by
immediate and multiple dysfunctions due to stroke and other symptoms of
concomitant diseases (Bond 1991, Piamarta et al. 2004). Alternative ways, such as
self-completing postal questionnaires and telephone interviews of collecting
depression and health data from older Chinese were infrequently used because of the
low response rate and because older people were often guarded about disclosing
personal information over the telephone as mentioned above.
Validation of data. Questionnaires addressing medical, technical and clinical data
might be hard to understand or easily misinterpreted by older Chinese with minimal
35
education. The problem might be further aggravated if the participants had suffered
from mild cognitive impairment as a consequence of stroke. This further emphasized
the need for a skilful interviewer to produce consistent and valid data
Each participant was interviewed twice, or three times at most. The inception
cohort was interviewed by the same research nurse twice: at one month and six months
after first admission to the stroke unit. The nurse had 20 years’ working experience in
clinical practice, research and gerontology. The use of a third interview was subject to
the findings of depression status among the subjects. Confirmation of diagnosis was
through consultation by a psycho-geriatrician (interview form in Appendix 2.3), blind
to the emotional status of the subject. A mixed group of patients who were either
depressed or from a randomly selected pool of non-depressed participants would be
interviewed. This interview would take place after the second interview.
36
2.3 Sampling
‘How well a sample represents a population depends on the sample frame, the
sample size, and the specific design of selection procedures.’ (Fowler 1993, pp.10)
2.3.1 Target populations
The sampling frame of the study was the Eastern district of Hong Kong Island,
one of the 18 districts in the Territory. Due to the small size of Hong Kong and the
mobility of the population, as well as the non-exclusive nature of the eastern district,
the assumption was made that the sample is representative of Hong Kong Chinese. It
might also be justified as the residents’ profile is found in the middle range as shown in
Table 2.1. The regional hospital in which the eventual sample was recruited serves a
population of 590,400, 7.6% of the whole population (Hong Kong Census and
Statistics Department 2004). The target population was the consecutive patients
admitted to the designated regional hospital serving the Eastern district with a primary
diagnosis of ischemic stroke for the first time. In this region, 6.1% of the residents had
not had any schooling, 61% were married with the rest ‘never married’ (30.3%),
‘widowed’ (6.2%) or ‘divorced/separated’ (2.5%) (Hong Kong Census and Statistics
Department 2004). The age and gender distribution of the target populations in the
sampling frame was compared with other districts of Hong Kong as shown in Table
2.1 below.
Because of considerations of resources, vast differences in new admissions and
the clinical management of stroke patients, the sample studied was captured from the
larger regional hospital only and the subjects recruited in another hospital and at an
earlier phase were to be used to compile the pilot study data to improve the study
design.
37
Table 2.1 Comparing the profile of sampling frame with other districts in Hong Kong.
Mean for 18
Eastern district
Minimum
Maximum
Sex ratio (per 1000 women)
847
766
998
929
3
Median age
40
34
40
38
13-18
Age range (25-64)
61.1
56.4
63.9
60
11-13
Age range (65+)
13.3
6.8
17.6
11.7
13
districts
Rank (1=lowest)
To evaluate a sampling frame, comprehensiveness, probability of selection and
efficiency should be taken into account. As 90% of hospital admissions were taken up
by the public sector, leaving only 10% attending private hospitals, in particular for
serious disease episodes in Hong Kong (Department of Community Medicine 1998).
The target hospital was an index hospital that was likely to have captured the majority
of significant stroke episodes in the region. The sampling frame was to be
comprehensive. Since admissions for stroke were concentrated in a stroke unit and
coordinated by a stroke nurse, probability of selection could be ensured. To obtain an
incidence, the recruitment of subjects within a one-year span was undertaken, to avoid
seasonal variations in admission patterns.
2.3.1.1 Inclusion criteria
Patients were recruited to the study if they: (1) spoke Chinese, (2) were 50 or over,
and (3) had been admitted during the period of 1 June 2004 to 31 May 2005, (4) had
first-ever ischemic stroke diagnosed by a neurologist. The diagnosis would be
confirmed by the cardinal signs and symptoms of stroke attack using the International
Classifications of Diseases Coding System, ninth edition (those between 434.00 and
436.00) (National Center for Health Statistics 2003) and/or supported by computerised
tomography (CT scan) or magnetic resonance imaging (MRI) reports or discharge
summary.
38
2.3.1.2 Exclusion criteria
Exclusion criteria applied to patients who had: (1) a history of depression or other
psychiatric disease before the onset date of stroke, (2) a history of brain damage,
transient ischaemic attack or hemorrhagic stroke, (3) severe cognitive impairment as
evidenced by a score on the Abbreviated Mental Test of 5 or below during admission,
and (4) critical health status (such as a comatose state) with a score of 12 or below on
the Glasgow Coma Scale.
2.3.2 Sampling method
Consecutive first-ever ischemic patients formed the inception cohort. The
medical notes were read to counter-check fulfilment of the inclusion and exclusion
criteria for admission to the study after patients had given their consent. Most patients
were captured during the hospital stay after acute admission to the stroke units. Some
were discharged patients who had only a short hospital stay. They were contacted by
telephone for preliminary verbal consent. Formal consent would be obtained when the
interviewer met the potential participants at the first follow-up session. This group of
participants would be interviewed right away after consent.
2.3.3 Sampling size
Sample size was estimated using EPINFO software (Centers for Disease Control
and Prevention 2005). Taking 5% as the level of significance, 80% as the power and
allowing 3-5% margin of error to predict 10% incidence/prevalence (which is on the
low side compared with previous studies), only 137 subjects was estimated. Taking
into account dropouts and refusals, 50% more cases were added and the sample size
was rounded up to 210 (see Table 2.2).
In Hong Kong, more than 90% of hospital services are provided by the Hospital
Authority (HA) which is wholly funded by the Government. Hospitalisation for acute
stroke accounted for more than 25,700 in-patient discharges and deaths in all hospitals
and 3,434 deaths in 2005 (Centers for Disease Control and Prevention 2005, Center of
39
Health Protection 2006). Of all stroke episodes, roughly 70% are the ischemic type
and 30% haemorrhagic lesions (Yu 2003).
The recruitment was possible as yearly admissions per hospital are estimated to
be more than a thousand. Even after the application of the inclusion and exclusion
criteria, the sample size was achievable bearing in mind previous annual hospital
admission records for stroke cases
Table 2.2 Sampling estimation using Epiinfo 2005
Population Survey or Descriptive Study Using Random < Not Cluster > Sampling
Population Size
2,000
Expected Frequency
10.00%
Worst Acceptable
15.00 %
Confidence Level
Sample Size
80 %
59
90 %
97
95 %
137
99 %
236
99.9 %
382
99.99 %
530
40
2.4 Data collection
2.4.1 Study outcomes - the incidence and predictors of DAS
An 8-page questionnaire was designed to incorporate various measurement tools
for variables in the study. It was referred to as the one-month questionnaire (Appendix
2.4). For the follow-up study that took place at six months from admission for
first-ever ischemic stroke, the six-month questionnaire (Appendix 2.5) was used.
2.4.1.1 Tools for diagnosing and quantification of depression in patients suspected
of DAS
Depression was the outcome variable to be identified for this study, and both
quantitative and qualitative data on depression would be sought. Measurement tools
were considered and chosen on the nature of the target group and the tools’
psychometric properties, in particular the reliability and validity of respective
instruments in the target sample.
Though numerous assessment tools were available and used by clinicians in
research and practice in Western literature, direct application to Chinese subjects was
cautioned as depression specificity had been an unresolved issue (Aben et al. 2001).
Complexity in assessing depression across culture were well documented, such as
insufficient knowledge of depression, lack of cultural specificity and diversity in
emotional expressions among older adults, inadequate communicative indicators to
aid in detection, especially during the initial phase, though verbal indications were
shown to be useful in achieving early detection and recognition (Robinson-Smith
2004). Researchers had therefore been careful about using tools that have been
validated and targeted at Chinese. Even so, there is few large scale validation exercise
in the world literature regarding the Chinese population in the past decades.
To obtain quantitative evaluation of DAS, we adopted four different means of
assessing depressive symptoms. Additional strategies were incorporated to collect
41
qualitative data for depression validation. Incidence of depression at two prospective
phases would be assessed by four instruments. The Diagnostic and Statistic Manual
(fourth version) was used as the gold standard to detect depression. For screening
purposes, the Geriatric Depression Scale (short form) with 15 items in Chinese (GDS),
the Center for Epidemiologic Studies Depression Scale (CESD) with 10 items in
Chinese and three smiley diagrams, which served as adjunct assessment, were used
concurrently with DSM IV. A detailed discussion justifying the use of these tools and
their psychometric properties are given in the following sections.
DSM IV.
The DSM IV (American Psychiatric Association 1994), a generally
accepted gold standard, was used in previous studies to study DAS (Rao et al. 2001,
Aben et al. 2003, Toso et al. 2004, Cassidy 2004). Subjects were identified as
depressed if they reported five out of the nine criteria, including either persistent low
mood or lack of interest in daily living activities in the past week. Use of DSM criteria
in detecting geriatric depression in community settings has been criticised for their
limited effectiveness as many older adults might not display full bloom of depressive
symptoms identified by DSM (Everson et al. 1998). At the same time, the use of
screening tools was known to have more false positive results. This argument was
supported by the findings of Madianos’s study in 1992 on the elderly population in
Athens (Madianos et al. 1992). Twenty-seven percent of subjects were screened to
have depression using a self-reporting questionnaire. But only 10% were considered to
have depressive disorder using the DSM criteria. A similar finding was noted by Liu’s
study (Liu et al. 1997). Only 13% out of the 26% who were screened positive using
GDS-SF were eventually diagnosed with depressive disorder using DSM III R criteria.
Thus two screening tools were added for the assessment and this argument will also be
tested in the current study.
GDS. The Geriatric Depression Scale was devised by Brink, Yesavage and other
colleagues (1982). The tool was also reported to out-perform those of the Zung
Depression Scale (Zung 1965) and Hamilton Rating Scale in Depression (Hamilton
1967) in differentiating depressed from non-depressed elderly clients (Nezu et al.
2000). The Chinese version of the short form, GDS-SF, was available and validated in
Hong Kong (Sheikh & Yesavage 1986, Chiu et al. 1994, Lee et al. 1994). Studies of
42
Chinese subjects had used 5 or 8 out of a total of 15 as cut-off points. However the best
rotating operating curve suggested 8 in order to yield the highest sensitivity (87.5%)
and specificity (92%) for depression assessment (Lee et al. 1994). Additionally, the
use of 8 as the cut-off point for the GDS was reported to have satisfactory reliability
and validity (Chiu et al. 1994, Lee et al.1994). In Hong Kong, GDS was widely
adopted as a screening instrument by many geriatric wards and incorporated as an
assessment routine and progress evaluation tool in psychiatric hospital units. As far as
research studies were concerned, most had adopted 8 as the cut-off point (Lee et al.
1994, Lesher & Beryhill 1994, Herrnann et al. 1996, McGivney et al. 1996). GDS was
recommended as a standard instrument for screening in geriatric populations by the
Royal College of Physicians and the British Geriatrics Society (Royal College of
physicians of London and the British Geriatrics Society 1992). It was suggested that
as GDS-SF correlated well with the Philadelphia Geriatric Center Morale Scale,
Southampton Self-Esteem Scale and Bradburn Affect Balance Scale, additional use of
such well-being measures might be unnecessary (Coleman et al. 1995). There had
been worries that impaired hearing and cognitive function, poor cooperation or altered
level of consciousness might affect the reliability of the screening results (Kwai
Chung Hospital Psychogeriatric Team 2000). But another study showed that the
sensitivity and specificity of the short or long forms of GDS were generally
maintained with either cognitively intact or impaired groups (Kwai Chung Hospital
Psychogeriatric Team 2000). Some clinical studies had adopted the tool for Caucasian
(Agrell & Dehlin 1989, Johnson et al. 1995, Mast 2004) or Chinese stroke patients
(Tang et al. 2004). The subjects were considered as depressed with a score of 8 or over
out of the 15 points in this study.
CESD. The Center for Epidemiologic Studies Depression Scale (CESD) had been
recently validated in Chinese (Radloff 1977, Boey 1999a). It is a 10-item scale and
subjects are given four answer options (0, 1, 2, 3; 0=none at all, 3=all the time in the
past week). Again, the tool has satisfactory reliability and validity among a geriatric
population. It was frequently used to detect DAS among Caucasian populations
(Agrell & Dehlin 1989, King et al. 2001, Robinson-Smith 2004, Cassidy et al. 2004)
but has not been tested among the local population.
43
2.4.1.2 Use of smiley diagrams to detect depression in patients suspected of DAS
In view of the practical difficulties in assessing this elderly Chinese population
with lower educational level and mostly physically disabled, a simple non-verbal
cultural independent tool not requiring sustained concentration or cognitive effort
was sought. A smiley pattern method was developed and described below. The utility
of the smiley pattern recognition as a simple first step assessment tool for depression
detection in this population has been published recently (see Publications).
This tool consisted of three enlarged diagrams, 3×3cm2: a smiley [
a flat face [
], and an upset/tearing diagram [
emoticon
emoticon
(happy),
(flat),
emoticon
(upset/tearing)
], one with
], henceforth referred to as
respectively. This was the first
attempt to use the tool on stroke patients to assess their mood status. The idea began
with a simple visual tool requiring less verbalisation compared with other depression
scales. The tool could help the mildly aphasic patients to validate and supplement
information in the assessment of depression.
Subjects were asked to rate the frequency of having these facial expressions over
the past week using a 4-point scale (0=none at all, 1=sometimes/1-3days per week,
2=most of the time/4-6 days per week, 3 all the time/ 7 days per week). Happy and sad
facial expressions were universally understood despite different cultural backgrounds
and literacy levels. The diagrams served to facilitate the comprehension and
expression of an emotional state among older adults, in particular those with low
education levels and difficulty in verbalising their moods in respect of a debilitating
disease.
2.4.1.3 Health literacy regarding depression
The complexity in detecting depression in this elderly Chinese population with
lower education level was also reflected in the little explored area of the concept of
depression and the relationship between heath literacy level and the report of
depressive symptoms. Qualitative data aims to explore the meaning that study subjects
44
give to events or experience that is of distinctive values to a study apart from the
quantitative data (Streubert & Carpenter 1999). To validate the diagnosis of depression,
additional methods were used to collect qualitative data, which would be referenced
by three parties: patient, research nurse and psycho-geriatrician.
All the cases identified as depressed using DSM IV at six months were invited to
see the psycho-geriatrician after the second interview. The only exceptions were those
who had already been under the care of a psychiatrist after stroke. This was because
most of these latter patients and their relatives had refused to have a second psychiatric
consultation, seeing it as redundant. 20% of non-depressed patients, randomly selected,
were also invited to see the psycho-geriatrician.
The study also captured qualitative data from the stroke patients in three ways.
First, participants were asked the question ‘what is depression to them’ at the
first-month and six-month interviews. Second, their emotional status and expressions
(tearful/sigh/lack of facial expression) were observed and recorded by the research
nurse at both interviews. Third, all retrievable medical records were studied
retrospectively for any documentation of mood status. The following information
areas would be considered: the presence of depression with documented signs and
symptoms as in DSM IV, any emotional complaints or observation by relatives of
self-worthlessness, hopelessness, frustration, self-pity, grief, guilt and shame; suicidal
ideation (expression of self-worthlessness, thoughts of death, concrete plans for
suicide, attempts at suicide); screening instruments or diagnostic tools for depression,
who reported the onset of depression (patient/relative/nurse/doctor); any prescription
of treatment options (psychoactive medication including anti-depressants, referral to
psychiatrist/psychologist/medical social workers/nurse specialist/others…) due to
emotional needs after stroke onset; and treatment duration.
Health literacy in respect of depression directly influenced early recognition of
the condition. We had very little information on patients’ literacy levels in respect of
depression despite the fact that it was known to be common among older Chinese
adults (Woo et al. 1994, Lee 2003) and stroke survivors (data published already, see
Publications). In fact, the search for subjective views on the situation of vulnerable
45
groups, including those aged 65+, mentally disabled or having low literacy levels,
had remained fruitless in the professional literature (Mika et al. 2005). More
importantly, a low level of literacy was proportional to mortality as well as
healthcare access among older people (Sudore et al. 2006).
All participants were asked at the end of the interview: ‘What is the meaning of
depression to you?’ and ‘How will depression be interpreted?’ Respondents were
encouraged to give any possible meanings to the term, and were reassured that they
would not be judged by their answers as there was no right or wrong answer, but only
their own perceptions. They were also asked if they would be interested in learning
more about depression.
2.4.1.4 The domain factors associated with DAS and predictors of DAS
The independent variables originated from the impact factors among the five
commonly affected domains post-stroke are:
1.
Bio-anatomical domain.
Data in this domain could be collected from stroke
related investigations, eg computerised tomography, magnetic resonance
imaging, magnetic resonance angiography, Carotid Doppler and serum blood
levels. Most of the data in this domain were at a nominal level of measurement
yielding binary (Yes versus No) answer options except that of blood results,
which maintains continuous measurement levels. Categories of data fell into the
following pattern:
z
Lesions location: left hemisphere/right hemisphere
z
Structural involvement: cortical/subcortical/brain-stem lesions; cranial
nerve involvement
z
Arterial system involvement: Large vessel diseases/small vessel diseases
z
Blood results: glucose, lipid profile, blood pressure
z
Cardiovascular risk factors: hypertension, diabetes, ischemic heart
disease, hyperlipidemia
46
2.
Cognitive and communicative domains.
Under this heading, two aspects of the
variables were examined. In the communicative domain, several items were
assessed for their level of disturbance using a 4-point scale. To assess cognitive
state and status, two standard tools were used: the Glasgow Coma Scale (GCS)
and the Abbreviated Mental Test (AMT; score range: 0-10). Patients who were
fully alert and conscious according to their verbal, motor and eye-opening
response sub-categories gained a full score of 15 on the GCS. The data were
collected from patient records documented in the Accident and Emergency
Department. Patients having a score below 12 were excluded from the study.
Cognitive soundness in terms of AMT was assessed at one-month and
six-month interview. Patients were considered cognitively intact with a score
between 6 and 10 out of a full score of 10. Those scoring lower than 6 were
excluded from the study during the consent stage.
In respect of communicative impairments, patients were asked to evaluate the
level of disturbance resulting from the following types: expressive aphasia,
receptive aphasia, global aphasia, dysarthria, swallowing, visual impairment,
hearing deficit and agnosia. A zero meant none at all, 1 indicated a mild or
infrequent level of disturbance (about once or twice weekly), a 2 referred to a
moderate level (about three or four times weekly), and a 3 meant a severe level
(five times or more on a weekly basis).
3.
Dependency and somatic symptoms domain.
In the disabilities and
dependency domain, we measured self-care functions by two validated scales,
another scale for quality of life with respect to disability, and a 4-point scale for
the level of disturbance of somatic symptoms. The two validated scales were:
the Barthel Index of Activities of Daily Living (BADL; 0-20, 20 being the
highest functional level) (Collin 1988, The Royal College of Physicians London
and The British Geriatrics Society 1992), and the Norton Scale of Pressure
Sores (NSPS; 5-20, 20 being the lowest dependency in skin-care and minimal
proneness to pressure sores) (Norton 1989, Pang & Wong 1998). These tools
were commonly used among local residents.
47
To assess the level of disturbance induced by somatic symptoms, five types of
pain and five kinds of bodily discomfort were included: headache, stomach pain,
joint pain, low back pain and nonspecific muscle pain; nausea, vomiting,
dizziness, constipation and other nonspecific discomforts. A 4-point scale was
used: 0 indicating none, 1=mild, 2=moderate and 3=severe levels of disturbance.
A similar scoring system was used to that described in the communicative
domain.
The patients’ crude quality of life was measured by the Modified Rankin Scale
for Quality of Life (MRSQoL). It was a 6-point scale ranging from 0 to 5 with 5
being the highest level of compromised quality of life (Rankin 1957, Farrell et al.
1991).
4.
Emotional domain. Besides the standardised tools used on depression, ten
additional items in this domain were assessed. Patients were asked about the
levels of disturbance caused by anxiety, worry about stroke rehabilitation
progress, worry about disease deterioration, life stressors such as levels of
disturbance due to financial problem, losing a job, bodily pain and discomforts,
poor family relationships, loss of loved ones. There was growing evidence of a
bio-chemical basis with respect to hypersecretion of adrenal glucocorticoids to
connect the occurrence of chronic stress with depression (Leonard 2006a).
Therefore the examination of level of disturbance with respect to different
stressors was indicated. The same 4-point scale was used as that in the
communicative domain. In addition, participants’ perceived current health status
and the level of disturbance due to worry about that status were explored under
this domain.
5.
Family and social support domains.
The Lubben Social Network Scale (LSNS)
in Chinese was used (Chi & Chou 2001, Lubben & Gironda 2003) to assess the
role of family and social support in depression. The scale consists of 10 items,
each having 5-point-answer options (1-5). It may be divided into three
sub-categories: (1) family network, (2) friends network, (3) confidant
48
relationship. The aggregate scores ranged from 0 to 50, 50 indicating maximal
perceived support. Some questions were put to the participants to probe the
availability of help with their emotional needs during a hospital stay due to
stroke. They were asked if any of the hospital staff or other people
(doctor/nurse/social worker/therapist /spouse/children/friends) might talk to
them about their emotions or moods, and how helpful they thought these people
were, using a 4-point scale (0=no use at all to 3=most helpful). Participants
were also assessed on their current use of strategies to relieve life stress or
unhappiness. Seven items were proposed, with an open-ended item to add
another possibility. As before, the effectiveness of various means was assessed
using a 4-point scale. The items included both indoor and outdoor activities.
Sleeping, watching TV or listening to the radio or music, drinking or smoking
or eating, doing household chores was regarded as an indoor activity. Going for
a walk, shopping, exercising or finding someone to chat with, was considered
outdoor activities.
Besides the five domain factors, socio-demographics and other clinical sequelae
were also examined. Participants’ age, gender, education, marital status, living
arrangement, financial support, pre-morbid data such as medical history, physical
health
condition,
health-related
life-style
factors
such
as
smoking,
and
institutionalisation that occurred before stroke onset were retrieved and recorded in the
questionnaire from patients’ case notes.
As depression can be related to side effects of drug use, the prescriptions of
current drugs used were recorded. Investigation and treatment modalities related to
depression were also noted, including medication, referrals to a psychiatrist,
psychologist, nurse specialist, medical social worker or other personnel. A process
review on the four aspects - recognition, detection, diagnosis and treatment for DAS
during the study period - was documented in the questionnaire as well (see Table 2.3).
49
Table 2.3 Process review agenda on DAS.
Key Category of
information
Who does
What
to Whom
When
How
Why
so What
z
z
z
z
Possible data to be identified
Research team, patients, relatives, healthcare professionals
Communications
Interventions (investigation, seeing relatives, observations) from case
notes
z
z
z
z
z
z
z
z
z
z
Words on mood that related DAS
DAS Patients and relatives or other health care members
Date and time from onset of stroke and DAS
Cues from patients or relatives to allow recognition of DAS
The methods / tools used to detect depression
Plans to treat or care patients with DAS
Follow up plan for DAS
Rationale for clinical decisions relating to DAS
Formal or informal; explicit or subtle
The outcomes to patients in terms of mortality, use of formal health
care or informal care, hospital admission, quality of life.
2.4.2 DAS interventions
Though it was a popular trend in healthcare intervention studies, the discussion of
such was considered secondary when answers to important primary questions were
still unaddressed. Based on the study outcomes, DAS interventions would be
discussed. It was hoped that the proposed interventions were relevant and realistic in
the eyes of local residents, as well critically appraised together with the evidence in
earlier literature.
2.4.3 Pilot study
A pilot study was particularly important because of the paucity of research on
DAS among Chinese in Hong Kong, and major changes were indicated at the piloting
stage. The aim was to polish the data collection tool to make them more user-friendly,
as well as to improve content and construct validity and streamline the data collection
process.
50
A 10% section (n=20) of the original estimated sample was planned for the pilot
study, which aimed at testing the feasibility of the whole set of questionnaires and the
reliability of respective depression assessments. A panel of investigators, academic
and clinical experts (a professor of psychiatry, a consultant psycho-geriatrician, a
professor of neurology and the teaching consultant in nursing) jointly evaluated the
face and content validity of the questionnaire. Other logistical arrangements for the
on-site studies were negotiated with clinical collaborators by the research nurse. The
clinical partners included: a consultant physician and nurse specialists from stroke
units and geriatric day hospitals, a ward manager of outpatient clinics, the
administrator in charge of the medical records office, and ward nursing staff. The
ultimate goal was to ensure the collection of reliable and valid data in an efficient
manner. The initial questionnaire was trimmed to be more user-friendly for the
participants, the interviewer and the researcher. The finalised questionnaires for the
one-month and six-month interviews were in Appendices 2.1 and 2.2. After the pilot
phase, the following revisions were made.
1.
The one-month 12-page questionnaire was cut down to 8 pages with two
main sections: the first four pages contained questions exploring the core
domain factors, while pages 5-8 covered different sets of validated tools
incorporated in the study.
2.
The format of the questionnaire was greatly revised to provide clearer but
simpler wording, layout and design.
3.
Logical sequencing was followed by allotting questions to the five domain
factors that might be associated with DAS, together with sensitive
questions such as those exploring life stressors and family relationships, to
be followed by socio-demographic data and health history.
4.
The exact wording for instructional use of the depression assessment tools
was incorporated into the questionnaire to maintain consistency in
interviewing.
5.
All questions and answer options were coded in a consistent numerical
format.
The most frequently selected items were assigned first place to
allow ease of data-entry and avoid false entries.
6.
The items: ‘date of interview’ and ‘subjective interpretation of depression’
51
were added.
7.
The items morale scale, esteem and self-concept, locus of control scale;
role deficit; Hospital Anxiety and Depression Scale and GHQ-12 were
removed. Many diseases under ‘medical history’ were moved to the ‘others,
specify’ section, except neurological, cardiovascular and psychiatric
diseases, to avoid excessive detail. Five smiley diagrams were reduced to
three. The answers of most questions including the smiley diagrams were
revised as follows:
z
Cutting most of the answer options from a 7-point Likert scale to a
4-point scale
z
Re-sizing to make bigger smiley diagrams for older subjects
z
Aligning the answer options that were used by clinical partners
z
Arranging the answer options according to frequency of selection
z
All dates for respective investigations were aligned with questions
for reference only, instead of serving as data.
8.
The target population was cut down from two regional hospitals that were
arbitrarily clustered by the Hong Kong Hospital Authority to a larger
regional hospital that serves a district on Hong Kong Island. The rationale
for this decision was as follows:
9.
z
Low recruitment rates in the smaller hospital due to higher recurrent
stroke admissions
z
Time and manpower involved major resource implications and
feasibility concerns
z
Data analysis on annual admissions (at the end of subject recruitment)
showed that there was a significant difference in the client profile of
stroke units in the two district hospitals (Appendix 2.6).
52
2.5 Data analysis
2.5.1 Data management
Data entry was undertaken with the use of Microsoft Access. Specifically, data
screen input forms were developed with data input controls to avoid entering
implausible values. After all data were entered, they were converted to the Excel
format and two research assistants independently cross-checked the entered data with
original hardcopy of the completed questionnaires. All discrepancies were handled
and resolved by the principal investigator. The final database was then read into SPSS
14.0 for Windows statistical analysis.
2.5.2 Patients’ demographic and health profiles
A broad description of the participants’ background in terms of their personal and
health profiles were analysed and presented. Specifically, frequency counts and
percentages would be shown for categorical data, for example, gender, marital status,
occupation, presence of a disease (yes/no; such as mental psychiatric
disease/hypertension/diabetes/cardiovascular disease/tumour), and the use of different
groups of medications. For continuous data, the mean and standard deviation were
calculated and presented (Last 2001). This applied to age, length of hospital stay and
years of formal education, physiological indicators such as blood sugar level or lipid
profile, total number of chronic diseases and aggregate number of pains or somatic
symptoms.
2.5.3 Validity of the assessment tools
The agreement between raters and rating scales was examined by using the kappa
coefficient. Kappa coefficient is an index of the degree of non-random agreement
between two categorical variables. It typically ranges from 0 indicating no agreement
to 1 indicating perfect agreement. 0.41-0.60 would be considered as a moderate
agreement (Chuang 2001). Taking DSM IV as the gold standard, a kappa coefficient
was calculated for its agreement with each of the other three assessment tools (Last
2001). Besides this, the test was also applied to validate the agreement in depression
53
diagnosis made between the psychiatrist and nurse. This analysis also helped the
calculation of the sensitivity, specificity, predictive values and likelihood ratios as
essential component to evaluate the screening properties of the tools. The likelihood
ratio was also included, as it is an index that has added value by not being directly
affected by prevalence of the disease as in the case of sensitivity, specificity or
predictive values (Sackett 1991).
2.5.4 Incidence of DAS at one month and six months
The incidence of DAS was assessed by calculating the percentage of depressed
patients. This percentage was calculated for each of three diagnosis methods of
depression: DSM, GDS and CESD. The corresponding 95% confidence intervals were
calculated using the formula ‘p±1.96 √(pq/n)’, where p=estimated incidence rate of
depression, q=(1-p), and n=number in the sample (Triola & Triola 2006).
Cross-tabulation was used to show the respective frequencies of depression among
male and female participants.
2.5.5 Risk factors and predictors of DAS
Potential factors associated with depression at one month and six months were
examined. Depression was identified by DSM IV. Potential factors of interest covers
five respective domains including (1) bio-anatomical domain - laterality of brain
lesions or side of neurological dysfunctions (left versus right), presence of circulatory
and
neurological
abnormalities
in
different
investigations
(computerised
tomography/Carotid Doppler), presence of cranial nerve involvement; (2) cognitive
and communicative domain – presence of different aphasia, visual or hearing problem,
memory problems; (3) dependency level and symptomatic symptoms domain –
presence of different pains or bodily discomforts; (4) emotional domain –presence of
different worries and life stressors, display of different facial expressions and anxiety,
perceived current health status (good or average versus poor), presence of different
mood conditions during hospitalization (depression/anxiety/ panic/worry/agitation);
(5) social support domain - any help with different coping methods (yes/no). Besides
the domain factors, the demographic variables and health factors were also examined.
54
In the case of factors presented as a continuous level of measurement, an
independent sample t-test (Student’s t-test) was used to identify significant differences
in group means between depressed and non-depressed groups. Factors that adopted
such a test included:
1.
Demographics - age, length of hospital stay and years of formal education;
2.
Health status or history - physiological indicators such as blood sugar level
or lipid profile and total number of chronic diseases;
3.
Dependency level and somatic symptoms domain –aggregate number of
pains or somatic symptoms; dependency level by Barthel Activities of
Living
(BADL)
or
Norton
Score
for
Pressure
Sore
(NSPS);
disability-related quality of life using the Modified Rankin Scale for
Quality of Life (MRSQoL);
4.
Cognitive domain –Abbreviated Mental Test (AMT);
5.
Emotional domain – frequency of displaying a happy/flat/upset or tearing
facial expression; and
6.
Social support domain – Lubben Social Network Scale (LSNS).
Effect of each categorical factor was examined by the Pearson chi-square test
while that of each continuous factor was examined by the two independent samples
t-test. Independent effects of all factors were examined by a multiple logistic
regression. All effects were expressed as an odds ratio. Adequacy of the logistic
regression analysis was assessed by performing the Hosmer-Lemeshow test with a p>
0.05 indicates no evidence of poor fit.
Multiple regression was indicated when the dependent variable fell on a
continuous level of measurement such that by GDS. In order to identify predictive
factors that could account for the depression score at six months as well as the change
in depression scores, linear regression was used. To test model fitness, model
adequacy was checked by examining the residuals. No departures for model
assumptions were found. The change in depression scores was yielded by the
55
following calculation: depression score 6M- depression score 1M. The GDS score was
chosen as the dependent variable in this section, as it provided a continuous level of
measurement of depression, and was thus more sensitive to changes at different time
points. Identification of predictors at one month might help to direct adequate attention
to high-risk patients or even to devise early intervention when DAS is detected.
2.5.6 Changes of domain factors from one month to six months
To examine the change of domain factors from one month to six months after
stroke, the paired t-test was used. Specifically, domain factors considered included
cognitive functions using AMT, level of disturbance due to communicative problems,
level of disturbance due to different types of pain or body discomfort, dependency
levels in terms of BADL, disability related quality of life MRSQoL, depression levels
in terms of GDS/CESD or frequency of displaying different facial expression, and
level of disturbance due to worry or anxiety. The changes were estimated by the means
together with the corresponding 95% confidence intervals. The normality assumption
of the paired differences was checked by using the P-P plot.
2.5.7 The clinical outcomes of stroke survivors
The clinical outcomes of current stroke survivors refers to the treatment of DAS
under formal healthcare, crude quality of life, mortality and institutionalisation after
first stroke, particularly at the chronic stage. The difference that might be identified
between depressed and non-depressed subjects was estimated mainly by Pearson
chi-square test, as most of these variables were categorical.
To identify predictors of quality of life at six months, multiple regression analysis
was performed. Predictors considered included from the respective five domains as in
DAS analysis. Multicollinearity was checked by examining the tolerance and residuals
were examined for model adequacy.
56
2.5.8 Qualitative analysis – depression literacy
The present study is not an intervention study, and only theoretical
recommendations based on current study results and those in the literature are
proposed. For data based on the current study, two possible directions to devise
interventions in DAS might be contemplated: (1) the utility of smiley diagrams as a
preliminary assessment for depression among older stroke survivors, and (2) the
assessment of depression literacy among stroke subjects. The screening properties of
smiley diagrams have been discussed in Section 2.5.3. The assessment of depression
literacy among stroke subjects yielded qualitative data. Qualitative data analysis
method for the verbal scripts/descriptors provided by stroke survivors on ‘what is
depression to them?’ is indicated.
The qualitative analysis process required the researcher to scrutinise the data
carefully, reading the data many times in a search for meaning and a deeper
understanding. As claimed by Morse and Field (1995), qualitative analysis is a
‘process of fitting data together, of making the invisible obvious, of linking and
attributing consequence to antecedents’ (p126). Qualitative study enhances
understanding of meaning, values and belief in constructs that are unlikely to be
revealed by a quantitative study design (Polit & C.T. 2004). Qualitative study served
as adjunct data to facilitate a more comprehensive understanding of depression after
stroke among participants. It might produce very important hints where mental health
promotion among older adults or stroke patients is concerned. Participants were asked
about their understanding of the term ‘depression’, and their perceptions of ‘what is
depression to them’, at the first-month and six-month interviews. The raw data
obtained in Chinese was produced in English by two translators separately. One
academic staff member and one researcher worked separately to delineate and
categorise the subjects’ interpretations of depression. This strategy was important as
peer review helps to enhance the quality of a category system (Polit & C.T. 2004).
Core emerging themes are deduced and categorised, and their implications discussed.
57
Chapter 3 Results
3.1
Subject recruitment (59)
3.1.1
Inclusion and exclusion analysis (59)
3.1.2
Representativeness of recruited sample (59)
3.1.3
Descriptive analysis of participants (63)
3.1.3.1
Demographics and medical history (63)
3.1.3.2
Bio-anatomical characteristics of stroke lesions (65)
3.1.3.3
Cognitive and communicative status (71)
3.1.3.4
Dependency level and somatic complaints (71)
3.1.3.5
Emotional status (72)
3.1.3.6
Family and social support (74)
3.2
Study outcomes (80)
3.2.1
Level of agreements between depression assessments (81)
3.2.1.1
The level of agreement between psychiatrists and the research
nurse (81)
3.2.1.2
The level of agreement between depression assessment tools (82)
3.2.2
Incidence of DAS (84)
3.2.3
Risk factors of DAS (86)
3.2.3.1
Using DSM IV as the assessment tool (86)
3.2.3.2
Using GDS/CESD as the assessment tool (100)
3.2.4
Predictors of DAS (106)
3.2.4.1
Using DSM IV as the assessment tool (106)
3.2.4.2
Using GDS/CESD as the assessment tool (108)
3.2.5
Health literacy of depression (113)
3.2.5.1
Descriptors in affective domain (114)
3.2.5.2
Descriptors in behavioural domain (118)
3.2.5.3
Descriptors in cognitive domain (119)
3.3
Clinical outcomes (121)
3.3.1
Post-stroke quality of life (121)
3.3.2
Institutionalisation and mortality (123)
3.3.3
DAS interventions in formal healthcare (124)
3.3.3.1
Use of anti-depressants (124)
3.3.3.2
Use of mental healthcare resources (124)
58
3.1 Subject recruitment
Among all subjects (n=816) admitted during the one-year study period (1 June
2004 to 31 May 2005), there were all together 839 admission episodes in the target
regional hospital. A list of eligible subjects was drawn from hospital admission records
at the stroke unit. The process of subject recruitment was followed according to a
pre-set protocol as shown in Figure 3.1.
3.1.1 Inclusion and exclusion analysis
During the study period, a total of 816 patients were diagnosed with stroke. 152
haemorrhagic stroke cases (18.6%) were excluded from the study, leaving 664 as
ischemic stroke patients (see Table 3.1). After applying the inclusion and exclusion
criteria, 49.6% of patients were excluded for the following reasons: 2.8% (23 out of
816) were aged below 50 years old, 1.5% were cases of ‘transient ischemic attack’,
11.4% were recurrent stroke cases, and 5.2% had problems in communicating. Among
the eligible pool, about a third, were not recruited for a combination of reasons noted
in the table, some eligible stroke patients could not be found or refused to participate in
the study (23.2%, 189 out of 816). Eventually, about half of all the eligible subjects
consented to participate in the study (27.2%, 222 out of 816).
3.1.2 Representativeness of recruited sample
A ‘goodness-of-fit’ Chi-square test was used to examine the level of
representativeness of the sample compared with the target population. There was no
significant age difference between ‘eligible and consenting’, ‘eligible and not
consenting’ and ‘excluded’ groups (F=0.811, p=0.445, see Table 3.2A). A significant
difference existed among the female groups (p=0.003) but not identified among male
subjects (p=0.401, see Table 3.3). Regarding gender distribution, the proportion in
‘Eligible and consented’ female subjects was fewer (21.9%, p=0.007) than the two
counterparts (‘Eligible and did not consent’ or ‘Excluded’, see Table 3.2B). Overall
fewer females were recruited in the study (p=0.004, see Table 3.3).
59
Figure 3.1 A flow chart of subject recruitment
All stroke cases (1 June 2004 to 31 May
2005) in a regional hospital
N=816
Hemorrhagic stroke
N=152 (18.6%)
Excluded
First ischemic stroke
N=664 (81.4%)
Inclusion and exclusion criteria applied
Total excluded: 405 (49.6%)
Hemorrhagic stroke=18.6%
Age <50=2.8%
TIA=1.5%
Recurrent case=11.4%
Impair communication=5.2%
Others=10.1%
Excluded
Total: 189 (23.2%)
Can’t find pt (11.9%)
Refuse to participate
(11.3%)
Eligible
411 (50.4%)
Not consent
Consented
Total: 222 (27.2%)
Withdrew before
interview: 8 (1.0%)
Interview at 1 month (214)
Dropouts=26 (12.1%)
Passed away=11 (5.1%)
Away from HK=4 (1.9%)
Telephone changed=6 (2.8%)
Withdraw by self/relative=5 (2.3%)
ALL DAS + (20)
Interview at 6 month (188)
DAS -ve
Interviewed by Psychiatrist
A random sample of 34 (20%)
60
Table 3.1 Reasons for admission and exclusion of study subjects
Excluded reasons
n
%
Age <50
23
2.8
Transient ischemic attack
13
1.5
Recurrent stroke
93
11.4
Haemorrhagic stroke
152
18.6
16
2.0
19
2.3
7
0.9
(42)
(5.2)
Impaired communication
z
aphasic (moderate/severe)
z
language barrier
z
a
low cognitive level (AMT<6)
(Sub-total)
Other reasons for exclusion
z
secondary stroke due to complications of other diseases
3
0.4
z
death
12
1.5
b
z
poor general condition (GCS<10)
56
6.9
z
primary diagnosis is not stroke
1
0.1
z
history of depression or psychiatric illness
6
0.7
z
not Cantonese speakers
4
0.5
(82)
(10.1)
405
49.6
(Sub-total)
Total (excluded subjects)
Eligible subjects but consent to study not obtained
z
can't find the pt
97
11.9
z
subjects refuse to participate
92
11.3
(189)
(23.2)
214
26.2
8
1.0
(222)
(27.2)
816
100.0
(Sub-total)
Eligible and consent to study given
z
first interview conducted
z
withdrew before first interview
(Sub-total)
Total
a
AMT=Abbreviated Mental Test;
b
GCS=Glasgow Coma Scale
61
Table 3.2 Comparing age and gender groups of potential participants
(A) Age
Groups
n
Mean age
SD
df
F
p
Eligible and consented
222
72.1
9.4
2
0.811
0.445
Eligible and did not consent
189
73.3
9.5
Excluded
405
72.0
13.7
816
72.3
11.8
Total
One-way ANOVA was done
(B) Sex
Male
Female
n
%
n
%
df
χ
140
31.7
82
21.9
2
9.990
Eligible and did not consent
96
21.8
93
24.8
Excluded
205
46.5
200
53.3
441
100.0
375
100.0
Groups
Eligible and consented
Total
2
p
0.007
Pearson chi-square test was done
Table 3.3 Representativeness of the recruited sample
Population
Sample
N
%
n (observed)
%
n (expected)
Male
418
53.4
135
63.1
114.2
Female
365
46.6
79
36.9
99.8
Total
783
100.0
214
100.0
214.0
50-59
60
14.4
12
8.9
19.4
60-69
124
29.7
47
34.8
40.0
70-79
137
32.8
45
33.3
44.2
80-89
85
20.3
27
20.0
27.5
Gender (age 50+)
chitest
0.004
Age - Male
0.401
90+
12
2.9
4
3.0
3.9
Total
418
100.0
135
100.0
135.0
50-59
30
8.2
9
11.4
6.5
60-69
41
11.2
19
24.1
8.9
70-79
165
45.2
32
40.5
35.7
80-89
103
28.2
15
19.0
22.3
90+
26
7.1
4
5.1
5.6
Total
365
100.0
79
100.0
79.0
Age - Female
0.003
Age - Both
0.012
50-59
90
11.5
21
9.8
24.6
60-69
165
21.1
66
30.8
45.1
70-79
302
38.6
77
36.0
82.5
80-89
188
24.0
42
19.6
51.4
90+
38
4.9
8
3.7
10.4
Total
783
100.0
214
100.0
214.0
‘Goodness-of-fit’ Chi-square test was used
62
3.1.3 Descriptive analysis of participants
3.1.3.1 Demographics and medical history
The mean age of the 214 participants was 72.3 years (SD=9.4, range 50-94). The
average duration of hospital stay for stroke admission was 17.1 days (SD=15.4, range
3-89). The participants had had an average of only 5.5 years of formal education
(SD=5.0, range 0-23). About two thirds of the participants were male (63.1%, 135 out
of 214) and married (63.6%, 136 out of 214). Most were living with family members,
spouse or children or both (85.6%, 183 out of 214). Only a minority were living alone
(10.7%, 23 out of 214) or were still working (12.6%, 27 out of 214) at the time of the
stroke episode. In this sample, only five participants (2.3%) had been living in a care
and attention home or nursing home, as they required much nursing care. It was noted
that a quarter of the participants were dependent on public financial assistance such as
disability allowance (7.5%, 16 out of 214) or the combined social security allowance
(17.8%, 38 out of 214). The majority received financial support from family members
(63.6%, 136 out of 214), and others relied on savings (13.6%, 29 out of 214) or
pensions (4.7%, 10 out of 214, see Table 3.4). Thus, these patients are generally
under-educated and highly dependent on family and social support.
The medical history of the 214 participants is shown in Table 3.5. 69.6% of the
participants (149 out of 214) reported a history of hypertension; 29.9% (64 out of 214)
had diabetes; 36.9% (79 out of 214) had different cardiovascular diseases, including
hyperlipidemia. The majority had some chronic disease; the mean number of chronic
diseases reported was 2.1 (SD=1.2, range=0-6). Only 25 participants (11.7%) claimed
that they did not have any known chronic health problems before stroke (see Table
3.5).
63
Table 3.4 Demographic characteristics of 214 participants
n
%
135
63.1
Gender
Male
Female
79
36.9
Occupation before stroke
Retired
128
59.8
Housewife
52
24.3
Unemployed
7
3.3
Others working status
27
12.6
Married
136
63.6
Widowed
61
28.5
Single/Divorced/Separated
17
7.9
Spouse
42
19.6
Martial status
Living with
c
Living at
Children
51
23.8
Both spouse & children
90
42.2
10.7
Alone
23
Others
8
3.7
Public estate
87
40.7
Private premise
114
53.3
8
3.7
Rental
C&AH/NH
Financial assistance
Nil
OAA
b
c
5
2.3
133
62.1
27
12.6
16
7.5
38
17.8
Children
105
49.1
Spouse
19
8.9
Both children & spouse
12
5.6
Saving
29
13.6
Pension
10
4.7
Others
39
18.2
DA/HDA
CSSA
Financial Support from
a
d
Age (in years), mean±SD (range)
72.3±9.4 (50-94)
Length of stay (in days), mean±SD (range)
17.1±15.4 (3-89)
Formal education (in years), mean±SD (range)
a
5.5±5.0 (0-23)
Care and Attention Home (C&AH)/Nursing Home (NH)
b
Old Age Allowance
c
Disability Allowance / High disability Allowance
d
Combined Social Security Allowance
64
Table 3.5 Medical history of 214 participants
n
%
Diseases in medical record
Stroke
--
--
Psychiatric disease
--
--
Neurological disease (exclude stroke)
a
Hypertension (HT)
7
3.3
149
69.6
Diabetes mellitus
64
29.9
Cardiovascular diseases (exclude HT)
79
36.9
Tumor
15
7.0
Major operation done
45
21.0
Others
94
43.9
Number of chronic diseases
0
25
11.7
1-2
103
48.1
3-6
86
40.2
Smoker
53
24.8
Non-smoker/ex-smoker
161
Smoking status
Number of chronic diseases, mean±SD (range)
a
75.2
2.1±1.2 (0-6)
Parkinsonism
3.1.3.2 Bio-anatomical characteristics of stroke lesions
This section provides information regarding the characteristics of stroke lesions
as reflected by radiological and circulatory findings, and by biochemical and
neurological assessments. Among the 214 participants, about 80% (171 out of 214)
were diagnosed with cerebral thrombosis (ICD 434.00) upon discharge (see Table
3.6A). All participants had undergone computerised tomography (CT) after first stroke.
About two-thirds (67.3%, 144 out of 214) of the records had detected abnormalities,
with the rest deemed unremarkable. Most of the stroke lesions were one-sided
according to the clinical and/or radiological findings documented in case notes. Only
4.7% (10 out of 214) had lesions in both hemispheres and 9.3% did not remark the
laterality of lesions (20 out of 214). Of one-sided lesions, 46.7% (100 out of 214) were
located in the right hemisphere and 39.3% (84 out of 214) in the left. Most of the
medical notes did not document the locations of brain lesions. Of the left-sided lesions,
66.2% (53 out of 80) were in the sub-cortical region, 16.2% (13 out of 80) in the brain
stem, 12.5% (10 out of 80) in the cortical region and 3.8% in the cerebellar region. Of
right-sided lesions, 65.5% (59 out of 90) were located in the sub-cortical region,
15.5% in the cortical region, 10.1% in the brain stem and 7.8% in the cerebellar region.
About one-fifth of the participants also had MRI (14.6%, 31 out of 212) or MRA
(4.3%, 9 out of 212, see Table 3.6A).
65
Table 3.6 Characteristics of stroke lesions among participants at one month
(A) Imaging report
N
%
ICD code on discharge
433.80
6
2.8
434.00
171
79.9
434.10
10
4.7
436.00
27
12.6
No abnormality
70
32.7
Abnormality recorded
144
67.3
Radiological report
Findings on Computerized Tomography
Laterality of lesions
Left
84
45.7
Right
100
54.3
Both
10
4.7
Not applicable/not remark
20
9.3
Cortical region
10
12.5
Subcortical region
53
66.2
Cerebellar
3
3.8
Brain stem
13
16.2
More than one site
1
1.3
Cortical region
14
15.5
Subcortical region
59
65.5
Cerebellar
7
7.8
Brain stem
9
10.1
1
1.1
Locations of left-sided lesions (n=80)
Locations of right-sided lesions (n=90)
More than one site
Additional imaging done
CT
2
0.9
MRI
31
14.6
MRA
9
4.3
TCD
1
0.5
Carotid Doppler
1
0.5
168
79.2
Not done
a
a
n=212
66
(B) Arterial assessment and neurological report
n
%
Arterial involvement
Carotid Doppler result
Not done
44
20.6
No abnormality
46
21.5
Abnormality recorded
118
55.1
6
2.8
Mild (1 to 49% cross-sectional area occluded)
102
47.7
Moderate (50 to 99% cross-sectional area occluded)
12
5.6
Occluded
3
1.4
Normal/Not done/Missing
97
45.3
Mild (1 to 49% cross-sectional area occluded)
88
41.1
Moderate (50 to 99% cross-sectional area occluded)
19
8.9
Occluded
5
2.3
102
47.7
LACI
104
48.6
PACI
23
10.7
TACI
15
7.0
POCI
22
10.3
Not classified
50
23.4
Missing
Carotid artery blockage – Left side
Carotid artery blockage – Right side
Normal/Not done/Missing
Clinical classification of stroke type
a
Neurological deficit
Side of functional deficit
Left
105
49.1
Right
80
37.4
Both
7
3.3
Nil
22
10.3
Cranial nerve involvement
a
Yes
82
38.3
No
132
61.7
Had included one subject with uncertain result under unclassified
b
LACI=Lacunar cerebral infarction / PACI=Partial
anterior circulation infarction / TACI=Total anterior circulation infarction / POCI=Posterior circulation infarction
Besides the scan report, Carotid Doppler reports revealed that slightly more than
half (55.1%, 118 out of 214) had abnormal result, although the majority of these artery
blockages were mild without significant clinical implications (see Table 3.6B).
Another clinical classification for stroke type by circulatory disruption showed that
about half the participants (48.6%, 104 out of 214) had lacunar lesions. 18% involved
the anterior cerebral circulation, with 10.7% classified as partial (23 out of 214) and
7.0% as total anterior circulation infarction (15 out of 214). 10.3% were classified as
posterior circulation infarction (22 out of 214, see Table 3.6B).
Neurological assessment revealed that 38.3% participants (82 out of 214) had
cranial nerve involvement. More left-sided body dysfunctions (49.1%, 105 out of 214)
than right-sided (37.4%, 80 out of 214) were detected and this finding also
67
corresponded to the laterality of brain lesions (see Tables 3.6AB). For the biochemical
status of participants, the blood level in case notes and medications in the discharge
summary for the first stroke admission was retrieved (see Appendix 3.1). Mean levels
of total cholesterol, low density lipoprotein and high density lipoprotein (in mmol/l)
were 5.3 (SD=1.4), 3.5 (SD=1.0) and 1.3 (SD=0.3) respectively. Fasting blood sugar
was 7.1 mmol/l (SD=2.8). As for drug prescriptions, aspirin was given to the majority
of patients (84.9%, 180 out of 214), followed by statin compound (65.6%, 139 out of
214) and anti-hypertensive agents (49.1%, 104 out of 214). The use of psychoactive
agents (night drug/anti-depressants/anxiolytic) at one month was uncommon and was
examined in section 3.3.3.
68
Table 3.7 Functional levels in cognitive and communicative domains at one month and six months
(A) Communicative functions
Level of disturbance
None
Level of disturbance due to
Mild
Moderate
Severe
Time
Total
n
%
n
%
n
%
n
%
1M
213
179
84.0
24
11.3
4
1.9
6
2.8
6M
188
185
98.4
2
1.1
1
0.5
0
0.0
1M
213
211
99.1
2
0.9
0
0.0
0
0.0
6M
188
188
100.0
0
0.0
0
0.0
0
0.0
1M
213
209
98.1
3
1.4
0
0.0
1
0.5
6M
188
188
100.0
0
0.0
0
0.0
0
0.0
Expressive aphasia
Receptive aphasia
Global aphasia
Dysarthria
1M
213
168
78.9
27
12.7
9
4.2
9
4.2
6M
188
184
97.9
3
1.6
1
0.5
0
0.0
1M
213
188
88.3
16
7.5
7
3.3
2
0.9
6M
188
182
96.8
5
2.7
1
0.5
0
0.0
1M
213
210
98.6
2
0.9
0
0.0
1
0.5
6M
188
0
0.0
0
0.0
0
0.0
0
0.0
Time
Total
n
%
n
%
n
%
n
%
1M
213
174
81.7
28
13.1
7
3.3
4
1.9
6M
188
178
94.7
6
3.2
3
1.6
1
0.5
1M
213
188
88.3
18
8.5
6
2.8
1
0.5
6M
188
186
98.9
1
0.5
0
0.0
1
0.5
1M
213
172
80.8
30
14.1
8
3.8
3
1.4
6M
188
179
95.2
6
3.2
3
1.6
0
0.0
Swallowing
Agnosia
(B) Cognitive and sensory functions
Level of disturbance due to
Visual acuity
Hearing
Memory
69
(C) Changes in cognitive and communicative functions from one month to six months
1M
Glasgow Coma Scale (not analysed at 6M)
6M
6M-1M
Mean
SD
Mean
SD
14.89
(0.54)
--
--
N
Mean
95%CI
p
AMT Total
8.96
1.30
9.10
1.40
186
0.15
-0.02, 0.31
0.083
Level of disturbance due to expressive aphasia
0.25
0.65
0.02
0.18
188
-0.23
-0.32, -0.14
<0.001
Level of disturbance due to dysarthria
0.35
0.76
0.03
0.19
188
-0.32
-0.43, -0.22
<0.001
Level of disturbance due to swallowing problem
0.17
0.53
0.03
0.22
188
-0.13
-0.21, -0.05
0.001
Level of disturbance due to visual acuity
0.27
0.62
0.08
0.37
188
-0.19
-0.28, -0.09
<0.001
Level of disturbance due to hearing problem
0.16
0.48
0.02
0.23
188
-0.14
-0.21, -0.08
<0.001
Level of disturbance due to memory problem
0.27
0.61
0.06
0.30
188
-0.21
-0.30, -0.11
<0.001
70
3.1.3.3 Cognitive and communicative status
Communicative disturbances resulted from stroke were reported at one month.
The commonest was dysarthria (21.1%, 45 out of 213), followed by expressive
aphasia (16.0%, 34 out of 213). 11.7% of subjects had swallowing problems (25 out of
213). These complaints dropped drastically to 0 to 6 cases only at six months (see
Table 3.7A). 18.3% of participants (39 out of 213) reported visual problems, 19.2%
(41 out of 213) memory problems, and 11.7% (25 out of 213) some loss of hearing. All
these problems decreased to no more than ten at six months. The mental state
assessment by the Abbreviated Mental Test (AMT) showed a mean score of 9.0 out of
10 (SD=1.3) at one month and 9.1 (SD=1.4) at six months. The conscious level of the
participants was nearly full as reflected in the score on the Glasgow Coma Scale (14.9
out of 15, see Table 3.7B). Except AMT, there was a significant change in memory and
most of the communicative functions from one month to six months (see Table 3.7C).
3.1.3.4 Dependency level and somatic complaints
The self-care functions of participants measured by the Barthel Activity of Daily
Living Index (BADL) and the Norton Scale of Pressure Sore were 15.8 (SD=5.3) and
16.9 (SD=2.4) respectively at one month. At six months BADL was 17.0 (SD=4.7).
The Rankin Scale of Quality of Life at one month and six months was 2.4 (SD=1.3)
and 2.1 (SD=1.2) respectively (see Table 3.8A).
As for pains and somatic symptoms at one month, more participants reported
joint and low back pains with 22% reported joint pain (47 out of 214) and 17.3% low
back pain (37 out of 214, see Table 3.8B). About 10% and 8% reported headache and
stomach pain respectively at one month. At six months, the various forms of pain
dropped to 6.9% headaches, 3.2% stomach pain, 10.6% joint pain and 11.7% low back
pain (see Table 3.8B). The amount of pain reported was much reduced and levels were
regarded as mild at six months. The only exception to the falling trend was
non-specific muscle pain, which became more frequent at six months (19.7%, 37 out
of 188) compared with the rate at one month (11.2%, 24 out of 214, see Table 3.8B).
71
Among other somatic complaints, dizziness (17.8%, 38 out of 214 at 1M; 10.6%, 20
out of 188 at 6M) and constipation (18.2%, 39 out of 214 at 1M; 9.0%, 17 out of 188 at
6M) were more common at both time points. The occurrence of other somatic
complaints (nausea and diarrhea) was about 3% at one month and 0-1% at six months
(see Table 3.8C).
Table 3.8 Dependency level and somatic symptoms at one month and six months
(A) Self care function
1M
6M
Variables
Mean (SD)
Mean (SD)
N
Mean
95%CI
p
Barthel ADL Index (Range=0-20)
15.8 (5.3)
17.0 (4.7)
188
1.3
0.71, 1.79
<0.001
2.4 (1.3)
2.1 (1.2)
188
0.2
0.08, 0.36
0.002
16.9 (2.4)
--
0.7 (1.0)
0.5 (0.9)
188
-0.2
-0.34, -0.03
0.018
0.5 (0.8)
0.3 (0.5)
188
-0.2
-0.32, -0.09
0.001
1.2 (1.4)
0.8 (1.1)
188
-0.4
-0.58, -0.19 <0.001
Rankin Scale of Quality of Life (MRSQoL)
(Range=0-5)
Norton Scale of Pressure Sore (Range=5-20)
a
6M-1M
Somatic problems
Number of pains (Range=0-5)
Number of body discomfort (exclude pains)
(Range=0-5)
Total number of body discomfort and pains
(Range=0-10)
a
Item not assessed at six months; Paired samples t-test was done
3.1.3.5 Emotional status
Emotional assessment included worries, anxiety, perceived health and frequency
of different facial expressions. Findings revealed the occurrence and disturbance
induced by different stressors at one month in descending order: worry about stroke
rehabilitation (59.3%, 127 out of 214) and disease deterioration (32.2%, 67 out of 208),
level of disturbance due to body pains and discomfort (28.0%, 60 out of 214), financial
problems (26.6%, 57 out of 214), poor family relationship (11.7%, 25 out of 214), loss
of jobs (9.4%, 21 out of 214), current health status or other problems (7.5%, 16 out of
211) and loss of loved ones (4.8%, 10 out of 206, see Table 3.9A). A reduced
frequency of the three most common stressors was found at the six-month interview:
body pains and discomfort (25.5%, 48 out of 188), worry about stroke rehabilitation
(21.8%, 41 out of 188) and financial problems (21.3%, 40 out of 188, see Table 3.9A).
Besides these stressors, participants also reported anxiety at one month (23.2%, 47 out
of 203), although the level was much reduced at six months (5.9%, 17 out of 188, see
72
Table 3.9B). During hospitalisation for acute stroke attack, 214 participants reported
the presence of different types of emotions: depression (31.3%), anxiety (12.1%),
worries (33.2%), panic (20.1%), and agitation (7.0%, see Table 3.9B). The participants
also rated their perceived health status at one month and six months respectively.
Nearly half the participants perceived their current health status as bad at one month
(49.1%, 105 out of 203) but this dropped to 15.4% at six months (33 out of 188). On
the other hand, the choice of good health had risen from one fifth (21.0%, 45 out of
203) to one third of participants (34.6%, 74 out of 188), comparing one-month and
six-month ratings (see Table 3.9C). Some factors had showed a significant mean
change in scores when paired-sample t-test was done. Four factors had a significant
reduction from one-month to six-month ratings: perceived current health status
(p<0.001), days in the last week with an upset/tearing facial expression (p<0.001),
level of disturbance in worry about stroke rehabilitation (p<0.001), level of
disturbance in anxiety (p<0.001 see Table 3.9D).
When participants were asked about the frequency of displaying three different
facial expressions (happy, flat and upset/tearing) over the past week at one month and
six months interviews, 37.6% (80 out of 213) echoed the absence of a happy face at
one month, reducing to 26.6% (50 out of 188) at six months. Nearly two thirds of the
sample reported their flat-face frequency at one month as ‘half of the time’ (37.6%, 80
out of 213) or ‘all the time’ (27.7%, 59 out of 213). More than half of those reporting
‘all the time’ scored lower at six months (from 27.7% to 11.2%, see Table 3.10). 52%
participants reported the absence of an upset/tearing facial expression at one month,
while the rest would display such an expression at a different frequency (see Table
3.10).
73
3.1.3.6 Family and social support
A retrospective recall of available coping strategies towards unhappiness among
participants at the one-month interview revealed that many participants did not have
any means of tackling emotional problems before stroke. Many of them had tried the
following methods: (1) indoors - watching TV/listening to radio/music (43.9%),
finding someone to chat to (32.5%), sleeping (23.2%), drinking/smoking/eating
(15.6%) and doing household chores (12.3%). As outdoor interventions, most
preferred going for a walk (38.7%), exercising or 9.0% going shopping (see Table
3.11A). At six months, participants’ available social support was reflected in their
scores on the Lubben Social support Network Scale. The mean score was 20.4
(SD=8.0) out of 50. The subscales for family networks, relative networks and
confidant relationships were 4.7 (SD=3.7), 5.0 (SD=4.6) and 10.7 (SD=4.0)
respectively (see Table 3.11B).
74
Table 3.8 Dependency levels and somatic symptoms at one month and six months
(B) Various pains
Level of disturbance
None
Mild
n
Moderate
%
n
Severe
Time
Total
n
%
%
n
%
1M
212
191
90.1
9
4.2
6
2.8
6
2.8
6M
188
175
93.1
11
5.9
1
0.5
1
0.5
1M
214
196
91.6
9
4.2
6
2.8
3
1.4
6M
188
182
96.8
5
2.7
1
0.5
0
0.0
1M
214
167
78.0
30
14.0
10
4.7
7
3.3
6M
188
168
89.4
13
6.9
4
2.1
3
1.6
1M
214
177
82.7
28
13.1
5
2.3
4
1.9
6M
188
166
88.3
16
8.5
6
3.2
0
0.0
1M
214
190
88.8
14
6.5
6
2.8
4
1.9
6M
188
151
80.3
29
15.4
8
4.3
0
0.0
Headache
Stomach pain
Joint pain
Low back pain
Non-specific muscle pain
Row % were presented
75
(C) Somatic symptoms
Level of disturbance
None
Mild
Moderate
Severe
Time
Total
n
%
n
%
n
%
n
%
1M
214
176
82.2
21
9.8
9
4.2
8
3.7
6M
188
168
89.4
16
8.5
3
1.6
1
0.5
1M
214
207
96.7
5
2.3
1
0.5
1
0.5
6M
188
188
100.0
0
0.0
0
0.0
0
0.0
1M
214
208
97.2
5
2.3
0
0.0
1
0.5
6M
188
187
99.5
1
0.5
0
0.0
0
0.0
Dizziness
Nausea
Diarrhoea
Constipation
1M
214
175
81.8
26
12.1
11
5.1
2
0.9
6M
188
171
91.0
15
8.0
2
1.1
0
0.0
1M
214
196
91.6
11
5.1
1
0.5
6
2.8
6M
188
172
91.5
13
6.9
0
0.0
3
1.6
Others, specify
Row % were presented
76
Table 3.9 Emotional status at one month and six months
(A) Worries and life stressors
Level of disturbance
0
2
3
Time
Total
n
%
n
%
n
%
n
%
Worries about current health status
1M
211
195
92.5
4
1.9
6
2.8
6
2.8
6M
188
161
75.2
14
6.5
9
4.2
4
1.9
Worries about stroke rehabilitation
1M
214
87
40.7
28
13.1
35
16.4
64
29.9
6M
188
147
78.2
17
9.0
16
8.5
8
4.3
Bodily pains and discomfort
1M
214
154
72.0
25
11.7
15
7.0
20
9.3
6M
188
140
74.5
19
10.1
23
12.2
6
3.2
Financial problems
1M
214
157
73.4
18
8.4
18
8.4
21
9.8
6M
188
148
76.6
22
11.7
13
6.9
5
2.7
Loss of jobs
1M
214
193
90.6
8
2.6
5
2.3
7
3.3
6M
188
169
79.0
7
3.3
10
4.7
2
0.9
Poor family relationship
1M
214
189
88.3
11
5.1
6
2.8
8
3.7
6M
188
175
93.1
5
2.7
6
3.2
2
1.1
Loss of love ones
1M
206
196
95.2
4
1.9
5
2.4
1
0.5
6M
188
181
96.3
4
2.1
2
1.1
3
0.5
1M
211
195
92.5
4
1.9
6
2.8
6
2.8
6M
188
161
75.2
14
6.5
9
4.2
4
1.9
1M
208
141
67.8
18
8.7
18
8.7
31
14.9
6M
--
--
--
--
--
--
--
--
--
Other problems
Worries about disease deterioration
0=No disturbance at all
a
1
a
1=Mild disturbance 1 to 2 days a week
2=Moderate disturbance 3 to 4 days a week
3=Severe disturbance more than 5 days a week
Item not assessed at 6 month
Row % were presented
77
(B) Other emotions
Level of disturbance
0
1
2
3
Time
Total
n
%
n
%
n
%
n
%
1M
203
156
76.8
26
12.8
11
5.5
10
4.9
6M
188
171
94.1
8
4.2
8
4.2
1
0.5
Anxiety
Reported emotions during hospitalization, n (%)
Worry
1M
214
71
(33.2)
Depression
1M
214
67
(31.3)
Panic
1M
214
43
(20.1)
Anxiety
1M
214
26
(12.1)
Agitation
1M
214
15
(7.0)
0=No disturbance at all
1=Mild disturbance 1 to 2 days a week
2=Moderate disturbance 3 to 4 days a week
3=Severe disturbance more than 5 days a week
(C) Perceived current health status
Good
Average
Bad
Time
Total
n
%
n
%
n
%
1M
214
44
20.5
64
29.9
105
49.1
6M
188
74
34.6
81
37.9
33
15.4
(D) Changes in emotional status and life stressors from one month to six months
1M
6M
6M-1M
Mean
SD
Mean
SD
N
Mean
95%CI
p
Days in the last week with a happy facial expression
1.9
-1.0
2.0
-0.8
Days in the last week with an flat facial expression
1.8
-1.0
1.8
-0.7
188
0.1
-0.10, 0.22
0.469
188
-0.1
-0.20, 0.11
Days in the last week with an upset/tearing facial expression
0.8
-0.9
0.4
0.539
-0.6
188
-0.4
-0.52, -0.26
<0.001
Perceived current health status
2.3
-0.8
1.8
-0.7
188
-0.5
-0.62, -0.36
<0.001
1.4
-1.3
0.4
-0.8
188
-1.0
-1.19, -0.79
<0.001
Level of disturbance due to of losing job
Level of disturbance due to anxiety
0.2
-0.6
0.2
-0.6
187
0.0
-0.11, 0.09
0.839
0.5
-1.1
0.1
-0.5
179
-0.3
-0.51, -0.15
<0.001
Life stressors
Level of disturbance due to worry about stroke rehabilitation
progress
Paired samples t-test was done; Row % were presented
78
Table 3.10 Frequency of displaying different facial expressions over the past week at one month and six months
Level of disturbance
0
1
2
3
Time
Total
n
%
n
%
n
%
n
%
1M
213
80
37.6
58
27.2
57
26.8
18
8.5
6M
188
50
26.6
85
45.2
50
26.6
3
1.6
1M
213
21
9.9
53
24.9
80
37.6
59
27.7
6M
188
4
2.1
54
28.7
109
58.0
21
11.2
1M
214
111
52.1
58
27.2
33
15.5
11
5.2
6M
188
137
72.9
34
18.1
17
9.0
0
0.0
Days in the last week with
a happy facial expression
a flat facial expression
an upset/tearing facial expression
0=Not at all;
1=Sometimes 1 to 3 days a week;
2=Most of the time 4 to 6 days a week;
3=All the time with daily or more occurrence;
Row % were presented
79
Table 3.11 Coping resources
(A) Resources to cope with unhappiness before stroke at one month
Degree of helpfulness
No help at all
%
Mild
n
Moderate
%
n
Help a lot
Total
n
%
n
%
Sleeping
211
162
76.8
36
17.1
7
3.3
6
2.8
Watching TV/Listening to radio/music
212
119
56.1
61
28.8
24
11.3
8
3.8
Indoor
Drinking/Smoking/Eating
212
179
84.4
18
8.5
10
4.7
5
2.4
Finding someone to chat
212
143
67.5
47
22.2
19
9.0
3
1.4
Being occupied by household chores
212
186
87.7
23
10.8
3
1.4
0
0.0
Outdoor
Going for a walk
212
130
61.3
67
31.6
10
4.7
5
2.4
Going shopping
212
193
91.0
14
6.6
3
1.4
2
0.9
Doing exercise
212
186
87.7
17
8.0
5
2.4
4
1.9
(B) Lubben Social Support Network scores at six months
n
Mean
SD
Range
188
20.4
8.0
4-42
Subtotal score of LSNS on family (0-15)
188
4.7
3.7
0-13
Subtotal score of LSNS on relatives (0-15)
188
5.0
4.6
0-15
188
10.7
4.0
0-19
Sum score of Lubben Social Network Scale (0-50)
Subtotal score of LSNS on confidant (0-20)
Row % were presented
80
3.2 Study outcomes
3.2.1 Level of agreements between depression assessments
3.2.1.1 The level of agreement between psychiatrists and the research nurse
To validate the compatibility of the assessment made by the nurse and that
diagnosed by the psychiatrist, we presented the result on level of agreement using
Kappa’s statistics. Major depression was diagnosed by the psychiatrist under formal
healthcare setting as referenced by the documentation in patients’ record that was
reviewed retrospectively. The referral to psychiatric consultation was made
independently under formal healthcare setting during the period one to four months
after stroke. They did not know the assessment result made by the research nurse
though they may know that the patient was a subject of this study as a copy the
patient’s consent form was in place. At six months, a pscyhogeriatrician who was blind
to nurse’s assessment result and patients’ background had met a pool of 42 patients
with mixed depressive state. Only two patients were identified depressed.
At one month, the level of agreement between the psychiatrist and the nurse was
high with a Kappa’s value 0.843. Among those identified depressed by psychiatrist,
the nurse was able to capture 87.5% and all in the non-depressed group using DSM IV
(Specificity=100%, see Table 3.12A). At six months, the sensitivity and specificity of
nurse’s assessment were 100% and 73.8% respectively. The estimated positive and
negative predictive values were 15.4% and 100%. The Kappa’s value dropped to 0.204
(see Table 3.12B).
81
Table 3.12 Incidence of depression at one month and six months
(A) Level of agreement for assessments at one month
Diagnosis by psychiatrist
Assessment made by
the research nurse
Depressed
N=8
Not depressed
N=5
DSM +
TP=7
FP=0
PPV=100%
DSM -
FN=1
TN=5
NPV=83.3%
Sen=87.5%
Spe=100%
13
True positive (TP); False positive (FP); False negative (FN); True negative (Evans & Whitney);
Sensitivity (Sen)=TP/(TP+FN); Specificity (Spe)=TN/(FP+TN);
Positive predictive value (PPV)=TP/(TP+FP); Negative predictive value (NPV) =TN/(FN+TN);
Pearson chi-square test conducted; Kappa’s value=0.843
(B) Level of agreement for assessments at six month
Diagnosis by psychiatrist
Depressed
Assessment made by
the research nurse
Not depressed
DSM +
TP=2
FP=11
PPV=15.4%
DSM -
FN=0
TN=31
NPV=100%
Sen=100%
Spe=73.8%
44
Pearson chi-square test conducted; Kappa’s value=0.204
3.2.1.2 The level of agreement between depression assessment tools
The use of different smiley pictures to assess depression after stroke is shown in
Table 3.13. Taking DSM IV as the gold standard, the sensitivity and specificity of the
three emoticons
(upset/tearing, flat and happy)
were 76.8%/76.5.%, 75.8%/44.3% and
49.5%/72.2% respectively. For GDS or CESD, the sensitivity and specificity were
83.8%/74.8%, 64.6%/91.3% respectively. The positive and negative predictive values
of the smiley pictures were shown in percentage form as follows: emoticon (upset/tearing)
=73.8/79.3; emoticon
(flat)
=54/68; emoticon
(happy)
=60.5/62.4. Those for GDS and
CESD were 74.1/84.3 (GDS) and 86.5/75.0; CESD using a cut-off point of 11. The
positive likelihood ratio of GDS and CESD was 3.3 and 7.4 respectively. Those for the
upset/tearing, flat and happy smiley diagrams were 3.3, 1.4 and 1.8. The Kappa’s value
was highly significant between DSM and GDS/CESD/emoticon (upset/tearing), with values
set at 0.581/0.571/0.531 respectively (see Table 3.13).
82
Table 3.13 Screening property of depression assessment tools at one month
DSM IV (fulfilling 3 criteria)
Total
Not depressed
Depressed
n=115
n=99
N=214
Freq. (%)
Freq. (%)
Freq. (%)
Sn
Sp
PPV
NPV
Kappa
(%)
(%)
(%)
(%)
PLR
0.531
76.8
76.5
73.8
79.3
3.3
0.196
75.8
44.3
54.0
68.0
1.4
0.219
49.5
72.2
60.5
62.4
1.8
0.581
83.8
74.8
74.1
84.3
3.3
0.571
64.6
91.3
86.5
75.0
7.4
In the past week, patients had demonstrated
An upset/tearing face
No
88 (76.5)
23 (23.2)
111 (51.9)
Yes
27 (23.5)
76 (76.8)
103 (48.1)
No
51 (44.3)
24 (24.2)
75 (35.0)
Yes
64 (55.7)
75 (75.8)
139 (65.0)
Yes
32 (27.8)
49 (49.5)
81 (37.9)
No
83 (72.2)
50 (50.5)
133 (62.1)
Not depressed
86 (74.8)
16 (16.2)
102 (47.7)
Depressed
29 (25.2)
83 (83.8)
112 (52.3)
105 (91.3)
35 (35.4)
140 (65.4)
10 (8.7)
64 (64.6)
74 (34.6)
A flat face
A happy face
GDS group (Cut-off at 5)
CESD group (Cut-off at 11)
Not depressed
Depressed
Sn = Sensitivity; Sp = Specificity; PPV = Positive predictive value; NPV = Negative predictive value; PLR = Likelihood ratio [Sn/(1-Sp)] Pearson Chi square test was used.
83
3.2.2 Incidence of DAS
The incidence of depression for one-month cohort was 29.0% (62 out of 214,
95%CI=22.9%, 35.1%, see Table 3.14), using DSM IV as the gold standard. Nearly
three fifths of the female participants (95%CI=30.9%, 52.7%) were depressed while
only one fifth of the males (95%CI=14.6%, 28.4%) reported so at one month. The
incidence of depression at six months dropped to about one third at six months (10.6%,
20 out of 188, 95%CI=6.2%, 15%). Depression among the stroke participants was also
assessed by two other scales, the Geriatric Depression Scale (GDS) and the Center for
Epidemiologic Studies Depression scale (CESD). The cut-off point used for GDS was
8 and for CESD 15. The incidence of depression identified by GDS was 26.6% (57 out
of 214, 95%CI=20.7%, 32.5%) and 16.0% (30 out of 188, 95%CI=10.8%, 21.2%) at
one month and six months respectively. The incidence of depression at one month and
six months identified by CESD was 22.9% (49 out of 214, 95%CI=17.3%, 28.6%) and
13.3% (25 out of 188, 95%CI=8.4%, 18.2%, see Table 3.14) respectively.
The mean score obtained from GDS was 5.2 (SD=3.9, range 0-15) at one month.
It dropped to 3.3 (SD=3.5, range 0-14) at six months. For CESD, the mean score was
8.4 (SD=7.0, range 0-27) at one month, reducing to 6.4 (SD=5.9, range 0-22) at six
months. Thus there was a significant reduction in depression as assessed by GDS and
CESD (p<0.001, see Table 3.15).
84
Table 3.14 Incidence of depression at one month and six months
Depression groups
Non-depressed
Total
n
Depressed
%
n
95% CI
%
DSM IV
IM
Total
214
152
71.0
62
29.0
22.9,
35.1
Male
135
106
78.5
29
21.5
14.6,
28.4
Female
79
33
41.8
46
58.2
30.9,
52.7
Total
188
168
89.4
20
10.6
6.2,
15.0
Male
118
111
94.1
7
5.2
1.2,
9.2
Female
70
57
81.4
13
18.6
9.5,
27.7
6M
DSM (3 Criteria or more)
IM
214
115
53.7
99
46.3
53.0,
39.6
6M
188
133
70.7
55
29.3
35.8,
22.8
GDS (CO at 8)
IM
214
157
73.4
57
26.6
20.7,
32.5
6M
188
158
84.0
30
16.0
10.8,
21.2
GDS (CO at 5)
IM
214
102
47.7
112
52.3
59.0,
45.6
6M
188
133
70.7
55
29.3
35.8,
22.8
CESD (CO at 15)
IM
214
165
77.1
49
22.9
17.3,
28.6
6M
188
163
86.7
25
13.3
8.4,
18.2
CESD (CO at 10)
IM
214
133
62.1
81
37.9
44.4,
31.4
6M
188
132
70.2
56
29.8
36.3,
23.3
Row % presented;
CO=Cut-off point
Table 3.15 Changes in the GDS and CESD mean scores at one month and six months
1M
6M
6M-1M
Mean (SD)
Mean (SD)
N
Mean
95%CI
p
GDS (Range 1M=0-15; 6M=0-14)
5.2 (3.9)
3.3 (3.5)
187
1.9
1.35, 2.38
<0.001
CESD (Range 1M=0-27; 6M=0-22)
8.4 (7.0)
6.4 (5.9)
187
2.1
1.26, 3.01
<0.001
Paired sample t-test conducted
85
3.2.3 Risk factors of DAS
3.2.3.1 Using DSM IV as the assessment tool
Socio-demographic factors and health history. By putting the demographic factors
into univariate analysis with the depression group using DSM IV as the gold standard,
four demographic factors were found to have an association with depression. They
were: length of hospital stay (OR=1.03, 95%CI=1.01, 1.05, p=0.001, see Table
3.16A), being female (OR=2.62, 95%CI=1.43, 4.81, p=0.003, see Table 3.16B), not
being married (OR=2.46, 95%CI=1.34, 4.50, p=0.003) and living alone or other
living arrangements instead of living with family (OR=2.32, 95%CI=1.06, 5.05,
p=0.032). Participants’ age or formal education, working status, living premises or
financial assistance received did not have such an association, and nor did health
factors, including the number of chronic diseases, a smoking habit or health history
(see Tables 3.16ABC).
Bio-anatomical domain.
The findings related to radiological, circulatory and
neurological assessment on depression are discussed in this section. The association
between anatomical lesions depicted by computerised tomography in terms of
laterality and location along with depression could not be established in this sample
(see Table 3.17A). And it was noted that fewer than 50% of clients had their lesion
location documented in the case notes, leading to a reduced number of total subjects
available for analysis in this section. Where circulatory or neurological functioning
was concerned, the lesions that involved both the total or partial anterior cerebral
circulation infarction were identified as having a weak association with depression
(p=0.028, OR=2.23, 95%CI=1.08, 4.57, see Table 3.17B). Though not shown in tables,
analysis of various serum levels (including low/high density lipoprotein, total
cholesterol, fasting blood sugar, blood pressure on admission) was conducted but was
not associated with depression, neither was any documented use of drugs in this study
(aspirin, statin, antihypertensive, antidiabetic prescribed upon discharge).
86
Cognitive and communicative domain. In this section, the effect of communicative
problems are reported, in terms of aphasia, visual acuity and hearing; as well as
cognitive functions in terms of memory, and the Abbreviated Mental Test score on
DAS. Visual impairment had a consistent effect on depression at both acute and
rehabilitative phases (p1m=0.002; p6m<0.001, see Table 3.18A), but those visual
problems were few in this sample, especially at six months (n=10). The number
reporting expressive aphasia dropped from 34 to 3, whereas for global aphasia it
changed from 4 to none at six months, indicating that these problems were not
common in the sample (see Table 3.18A). In the cognitive domain, the AMT score that
reflected subjects’ mental capacity demonstrated a significant inverse relationship
with depression. Thus, the higher the AMT score, the less susceptible to depression the
subjects were. The depressed group had a significantly lower score (poorer mental
capacity) in AMT than their counterparts. The mean AMT score of the depressed
group was 8.4 (SD=1.5) compared with 9.2 (SD=1.1) among non-depressed subjects
at one month (p<0.001, OR=0.62, 95%CI=0.49, 0.78, see Table 3.18B). Such a trend
persisted at six months with a further drop in the mean score in the depressed group
(p=0.001, OR=0.52, 95%CI=0.39, 0.69, see Table 3.18B).
Dependency and somatic symptoms domain.
In this section, the effect of pain,
somatic symptoms and self-care capabilities on depression was explored. As regards
physical health factors, pain was frequently found to be associated with DAS.
Among various types, joint pain was the only significant factor associated with DAS
at both one month (p=0.020) and six months (p=0.006, see Table 3.19A). Dizziness
was another somatic complaint reported constantly that might have a role in
depression (p=0.018 at 1M; p=0.028 at 6M). The somatic complaints aggregate also
showed a significant effect on depression at different time points (p=0.001 at 1M;
p=0.008 at 6M, see Table 3.19B). Most of the pain and symptoms subsided at six
months, the only exception being non-specific muscle pain, which had increased
from 11.2% to 19.7% (see Table 3.19A) despite an insignificant effect on depression.
87
Three more tools for assessing self-care function were related to depression. On
the Barthel ADL index, the depressed group had a lower mean score (mean=12.5)
compared with that of the non-depressed (mean=16.9, p<0.001, OR=0.86,
95%CI=0.81, 0.91, see Table 3.19B). A similar pattern was observed for the Norton
Scale of Pressure Sore (p<0.001). In this study, the subjects’ quality of life measured
by the Modified Rankin Scale for Quality of Life (MRSQoL) showed a significant and
strong relationship with depression (p<0.001, OR=1.95, 95%CI=1.49, 2.54,). And
MRSQoL remained low and unimproved among the depressed participants six months
after first stroke (mean=3.1 at 1M and 6M, see Table 3.19B).
88
Table 3.16 Relationship of socio-demographics and health history with DAS
(A) Socio-demographics (continuous data)
Age (in years)
Total
Non-depressed
Depressed
n=214
n=152
n=62
Mean
SD
Mean
SD
Mean
SD
OR
95% CI
p
72.3
9.4
71.5
9.4
74.1
9.3
1.03
0.99 , 1.06
0.071
Formal education (in years)
5.5
5.0
5.2
4.7
4.6
5.6
0.97
0.91 , 1.03
0.421
Length of hospital stay (in days)
17.1
15.4
14.9
14.6
22.4
16.0
1.03
1.01 , 1.05
0.001
No of chronic diseases
2.1
1.2
2.0
1.2
2.3
1.4
1.17
0.92 , 1.49
0.191
Student’s t-test used
(B) Socio-demographics (nominal data)
Total
Gender
Marital status
Living with
c
Financial assistance
%
n
%
n
%
Male
135
63.1
106
69.7
29
46.8
(Female)
79
36.9
46
30.3
33
53.2
136
63.6
106
69.7
30
48.4
78
36.4
46
30.3
32
51.6
187
87.4
130
85.5
57
91.9
(Working)
27
12.6
22
14.5
5
8.1
Family
183
85.5
135
88.8
48
77.4
(Others)
31
14.4
17
11.2
14
22.6
No
160
74.8
119
78.3
41
66.1
54
25.2
33
21.7
21
33.9
Married
Not working
(Yes)
a
b
d
Pearson chi-square test used
a
Depressed
n
(Not Married)
Occupation
Non-depressed
2
df
OR
95% CI
p
9.009
1
2.62
1.43 , 4.81
0.003
8.666
1
2.46
1.34 , 4.50
0.003
1.641
1
1.93
0.69 , 5.34
0.199
4.617
1
2.32
1.06 , 5.05
0.032
3.452
1
1.85
0.96 , 3.54
0.063
(Variable)=risk factor for depression
Not married: Widowed/Single/Divorced/Separated
Others: including those living alone
χ
d
b
Not working: Retired/ Housewife/Unemployed
c
Family: Spouse/Children/Both spouse & children;
Yes: Had received Old Age Allowance/Disability Allowance/High disability Allowance/Combined Social Security Allowance
89
(C) Health history
Total
Non-depressed
n
Had a history of
%
n
Depressed
%
n
%
χ
2
df
OR
95% CI
p
a
Hypertension
149
69.6
106
69.7
43
69.4
0.003
1
0.98
0.51 , 1.86
0.956
Diabetes mellitus
64
29.9
44
28.9
20
32.3
0.230
1
1.17
0.61 , 2.21
0.631
Cardiovascular diseases excluding hypertension
79
36.9
51
33.6
28
45.2
2.548
1
1.63
0.89 , 2.98
0.112
161
75.2
109
71.7
52
83.9
3.495
1
0.49
0.22 , 1.04
0.062
53
24.8
43
28.3
10
16.1
Smoking habit
Non-smoker
(Smoker)
Pearson chi-square test used
a
(Variable)=risk factor for depression
Analysis was done using binary groups, i.e. yes vs. no such history
Table 3.17 The association of bio-anatomical domain factors with DAS at one month
(A) Features of brain lesions
Non-depressed
Total
Depressed
n
%
n
%
n
%
Unremarkable findings
70
32.7
55
36.2
15
24.2
(Abnormality recorded)
144
67.3
97
63.8
47
75.8
Left
84
45.7
60
46.5
24
43.6
(Right)
100
54.3
69
53.5
31
56.4
Subcortical region/cerebellar and brain stem
69
86.3
50
89.3
19
79.2
(Cortical region/multiple sites)
11
13.8
6
10.7
5
20.8
Subcortical region/cerebellar and brain stem
75
83.3
51
85.0
24
80.0
(Cortical region/multiple sites)
15
16.7
9
15.0
6
20.0
χ
2
df
OR
95% CI
p
2.877
1
1.78
0.91 , 3.47
0.090
3.141
1
1.12
0.59 , 2.12
0.208
1.451
1
2.19
0.59 , 8.03
0.228
0.360
1
1.42
0.45 , 4.43
0.549
Findings on computerized tomography
Laterality of lesions
Locations of left-sided lesions
Locations of right-sided lesions
Pearson chi-square test used
(Variable)=risk factor for depression
90
(B) Circulatory and neurological findings
Non-depressed
Total
Depressed
n
%
n
%
n
%
102
87.2
74
89.2
28
82.3
15
12.8
9
10.8
6
17.7
0-49% occlusion
88
79.6
64
81.0
24
72.7
(>/=50% occlusion)
24
21.4
15
19.0
9
27.3
LACI/POCI/Not classified
175
81.8
130
85.5
45
72.6
(TACI/PACI)
39
18.2
22
14.5
17
27.4
132
61.7
97
63.8
35
56.5
82
38.3
55
36.2
27
43.5
105
56.8
73
56.2
32
58.2
43.2
57
43.8
23
41.8
Degree of occlusion in left Carotid Artery (diameter reduction)
2
df
OR
95% CI
p
0.953
1
1.70
0.58, 5.00
0.333
0.955
1
1.55
0.64, 3.76
0.331
4.952
1
2.23
1.08 , 4.57
0.028
1.131
1
1.39
0.75 , 2.53
0.288
0.138
1
1.13
0.60 , 2.11
0.709
a
0-49% occlusion
(>/=50% occlusion)
Degree of occlusion in right Carotid Artery (diameter reduction)
Classification a/c circulation
χ
a
b
Cranial nerve involvement
No
(Yes)
Side of functional deficit
a
Right
(Left)
Pearson chi-square test used
b
80
(Variable)=risk factor for depression
a
Total not adding up to 214 due to missing data
LACI=Lacunar circulation infarction; POCI=Posterior circulation infarction; TACI= Total anterior circulation infarction; PACI= Partial anterior circulation infarction
91
Table 3.18 The association of cognitive and communicative domain factors with DAS
(A) Communicative factors
Total
Non-depressed
Depressed
χ
2
df
OR
95% CI
p
0.006
1
0.97
0.38 , 2.44
0.940
Time
n
%
n
%
n
%
Swallowing problems
1M
25
11.7
18
11.8
7
11.5
6M
6
3.2
4
2.4
2
10.0
3.358
1
NA
--
0.067
Visual acuity problems
1M
39
18.3
20
13.2
19
31.1
9.419
1
2.99
1.45 , 6.11
0.002
6M
10
5.3
4
2.4
6
30.0
27.070
1
NA
--
<0.001
Hearing problems
1M
25
11.7
16
10.5
9
14.8
0.751
1
1.47
0.61 , 3.53
0.386
6M
2
1.1
1
0.6
1
5.0
3.295
1
NA
--
0.070
1M
41
19.2
24
15.8
17
27.9
4.086
1
2.06
1.01 , 4.18
0.043
6M
9
4.8
7
4.2
2
10.0
1.334
1
NA
--
0.248
Dysarthria
1M
45
21.1
28
18.4
17
27.9
2.332
1
1.71
0.85 , 3.42
0.127
6M
4
2.1
3
1.8
1
5.0
0.887
1
NA
--
0.346
Expressive aphasia
1M
34
16.0
21
13.8
13
21.3
1.823
1
1.69
0.78 , 3.63
0.177
6M
3
1.6
1
0.6
2
10.0
10.067
1
NA
--
0.002
Receptive aphasia
1M
2
0.9
1
0.7
1
1.6
0.451
1
NA
--
0.502
6M
0
--
--
--
--
--
1M
4
1.9
0
0.0
4
6.6
10.158
1
NA
--
0.001
6M
0
--
--
--
--
--
1M
3
1.4
2
1.3
1
1.6
0.033
1
NA
--
0.856
6M
0
--
--
--
--
--
Had disturbance due to
Memory problems
Global aphasia
Agnosia
Binary outcome was assessed (ie. Yes vs. no. and only those reported having such symptoms were tabled;
Pearson chi-square test used;
NA- Not applicable; OR was not estimated if cell empty/ row total =0-10
(B) Cognitive factor
Non-depressed
Total
Time
Depressed
Mean
SD
Mean
SD
Mean
SD
OR
95% CI
p
1M
9.0
1.3
9.2
1.1
8.4
1.5
0.62
0.49 , 0.78
<0.001
6M
9.1
1.4
9.3
1.2
7.5
2.0
0.52
0.39 , 0.69
0.001
Abbreviated Mental Test (0-10, 10=highest mental functioning)
Student’s t-test used
92
Table 3.19 The associations of dependency and somatic symptoms domain factors with DAS
(A) Somatic symptoms
Non-depressed
Total
Depressed
Time
n
%
n
%
n
%
No
1M
167
78.0
125
82.2
27
17.8
(Yes)
1M
47
22.0
27
17.8
20
32.3
No
6M
168
89.4
154
91.7
14
70.0
(Yes)
6M
20
10.6
14
8.3
6
30.0
χ
2
df
OR
95% CI
p
5.398
1
2.21
1.12, 4.33
0.020
8.826
1
4.71
1.57 14.19
0.006
2.909
1
1.89
0.90, 3.93
0.088
0.236
1
1.38
0.37, 5.17
0.627
3.735
1
2.31
0.97, 5.47
0.053
0.001
1
1.02
0.32, 3.26
0.970
5.580
1
2.36
1.14, 4.85
0.018
4.856
1
3.40
1.09, 10.66
0.028
2.087
1
1.70
0.82, 3.51
0.149
0.996
1
1.94
0.51, 7.44
0.326
Had disturbance due to
Joint pain
Low back pain
Non-specific muscle pain
Dizziness
Constipation
Pearson chi-square test used;
No
1M
177
82.7
130
85.5
47
75.8
(Yes)
1M
37
17.3
22
14.5
15
24.2
No
6M
166
88.3
149
88.7
17
85.0
(Yes)
6M
22
11.7
19
11.3
3
15.0
No
1M
190
88.8
139
91.4
51
82.3
(Yes)
1M
24
11.2
13
8.6
11
17.7
No
6M
151
80.3
135
80.4
16
80.0
(Yes)
6M
37
19.7
33
19.6
4
20.0
No
1M
176
82.2
131
86.2
45
72.6
(Yes)
1M
38
17.8
21
13.8
17
27.4
No
6M
168
89.4
153
91.1
15
75.0
(Yes)
6M
20
10.6
15
8.9
5
25.0
No
1M
175
81.8
128
84.2
47
75.8
(Yes)
1M
39
18.2
24
15.8
15
24.2
No
6M
171
91.0
154
91.7
17
85.0
(Yes)
6M
17
9.0
14
8.3
3
15.0
(Variable)=risk factor for depression
93
(B) Dependency levels and aggregate somatic symptoms
Time
Total
Non-depressed
N=214
n=152
Mean
SD
Mean
Depressed
n=62
SD
Mean
SD
OR
95% CI
p
Total no. of reported pain (0-5)
1M
0.7
1.0
0.5
0.9
1.0
1.3
1.57
1.18, 2.08
0.002
6M
0.5
0.8
0.5
0.8
0.9
0.9
1.50
0.96, 2.36
0.076
Total no. of reported symptoms excluding pains (0-5)
1M
0.5
0.8
0.5
0.8
0.9
0.9
1.61
1.11, 2.35
0.013
6M
0.3
0.5
0.3
0.5
0.6
0.8
2.75
1.32, 5.76
0.007
Total no. of body symptoms (0-10)
Barthel ADL Index
(0-20, 20=highest function)
Modified Rankin Scale for Quality of Life
(0-5, 5=Lowest QoL)
Norton Scale of Pressure Sore
(5-20, 20=Lowest risk)
Student’s t-test used;
a
a
1M
1.2
1.4
1.0
1.2
1.7
1.7
1.42
1.16, 1.75
0.001
6M
0.3
0.5
0.3
0.5
0.6
0.8
1.61
1.13, 2.29
0.008
1M
15.6
5.3
16.9
4.4
12.5
6.0
0.86
0.81 , 0.91
<0.001
6M
17.0
4.7
17.6
4.3
12.8
5.3
0.86
0.79, 0.93
0.001
1M
2.4
1.3
2.1
1.2
3.1
1.3
1.95
1.49, 2.54
<0.001
6M
2.1
1.2
2.0
1.1
3.1
1.0
2.21
1.44, 3.40
<0.001
1M
16.8
2.4
17.4
2.0
15.4
2.8
0.70
0.61, 0.80
<0.001
--
--
--
--
--
--
--
The item was not included in 6M questionnaire
94
Emotional domain. Besides physical and cognitive functioning, some factors under the
emotional domain were also found to correlate with DAS. The assessment of this domain
falls into three aspects: (1) presence of different worries and life stressors, (2) perceived
current health status and disturbance due to anxiety, (3) frequency of different facial
expression (happy/flat/upset or tearing) over the past week.
The majority of the stroke survivors had stressors and worries over various aspects.
Immediately after stroke, the most frequent were worries over stroke rehabilitation,
disturbance due to body pains and discomfort, and financial problems. As many as 82.3% of
depressed participants (51 out of 62) verbalised worries related to stroke rehabilitation while
only 50% reported such worries among the non-depressed group (76 out of 152, p<0.001,
see Table 3.20A). At six months, the proportion of participants reporting worries decreased
mostly to one half or less. The most significant life stressors at six months became
disturbance due to body pains and discomfort, (p=0.001), poor family relationships
(p=0.003), and worry about stroke rehabilitation among the depressed group (p=0.011, see
Table 3.20A).
Anxiety and perceived current health status were two factors associated with DAS at
two time points. 35.1% (20 out of 57) of the depressed group compared with 18.5% (27 out
of 146) of the non-depressed group (p=0.013, see Table 3.20B) reported disturbance due to
anxiety. More depressed subjects (67.7%, 42 out of 62) reported poor perceived health
compared with the non-depressed (41.4%, 63 out of 152, p<0.001). Such a difference in
perception persisted to six months (p<0.001), even though the total number of participants
reporting poor health had dropped from 105 (at one month) to 33 at six months (see Table
3.20B).
As regards frequency of displaying a happy face, about 51% (31 out of 61, see Table
3.20C) of the depressed group reported none compared with 32% of the non-depressed
group (49 out of 152, p=0.012) at the one-month interview. The proportion having a happy
face rose to 77.4% for the non-depressed but dropped further to 60.0% from 40.0% for the
depressed at the six-month interview (p=0.001, see Table 3.20C). As for the flat face option,
a majority of the non-depressed group reported ‘Yes’ (86.2%) and the whole of the depressed
95
group did so. When asked about the frequency of displaying an upset/tearing face (as
shown by the upset/tearing emoticon), 86.9% of the depressed group (53 out of 62) while
about a third of the non-depressed group (32.2%, 49 out of 152) admitted to having an
upset/tearing face over the last week (p<0.001). Such a trend persisted at six months with
a large disparity in the proportion having an upset/tearing face between the depressed and
non-depressed groups (p<0.001, see Table 3.20C). Subjects’ reports on having different
emotions during stroke hospitalisation might have an effect on DAS, as shown in Table
3.20C. Significantly more depressed subjects (62.9%, 39 out of 62) than non-depressed
(18.4%, 28 out of 152, p<0.001) suspected themselves of having depression during their
hospital stay. Agitation was another factor identified as having an association with
depression but not reported worry, anxiety and panic during hospitalisation (see Table
3.20D).
Family and social support domain.
No significant difference was noted in the
availability and use of the various means mentioned in Table 3.11 to cope with
unhappiness before stroke and depression at one month (not shown in table). Participants’
social networks assessed at six months revealed that there was a mild protective effect of
having better scores on the ‘relatives network’ subscale (OR=0.88, 95%CI=0.78, 0.99,
p=0.044, see Table 3.21) or the aggregate scores on the Lubben Social Network Scale
(OR=0.94, 95%CI=0.88, 0.99, p=0.045) but not with the ‘family network’ and ‘confidant
relationship’ subscales.
96
Table 3.20 The association of emotional domain factors with DAS
(A) Presence of worries and life stressors
Total
Time
Worry about stroke rehabilitation
Financial problems
Body pains and discomfort
Poor family relationship
Pearson chi-square test used;
Non-depressed
n
%
n
%
Depressed
n
%
No
1M
87
40.7
76
50.0
11
17.7
(Yes)
1M
127
59.3
76
50.0
51
82.3
No
6M
147
78.2
136
81.0
11
55.0
(Yes)
6M
41
21.8
32
19.0
9
45.0
No
1M
157
73.4
114
75.0
43
69.4
(Yes)
1M
57
26.6
38
25.0
19
30.6
No
6M
148
78.7
131
78.0
17
85.0
(Yes)
6M
40
21.3
37
22.0
3
15.0
No
1M
154
72.0
120
78.9
34
54.8
(Yes)
1M
60
28.0
32
21.1
28
45.2
No
6M
140
74.5
132
78.6
8
40.0
(Yes)
6M
48
25.5
36
21.4
12
60.0
No
1M
189
88.3
138
90.8
51
82.3
(Yes)
1M
25
11.7
14
9.2
11
17.7
No
6M
175
93.1
160
95.2
15
75.0
(Yes)
6M
13
6.9
8
4.8
5
25.0
χ
2
df
OR
95% CI
p
18.993
1
4.64
2.24, 9.57
<0.001
7.059
1
3.48
1.33, 9.09
0.011
0.718
1
1.33
0.69, 2.55
0.398
0.526
1
0.62
0.17, 2.25
0.472
12.686
1
3.09
1.64, 5.82
<0.001
13.985
1
5.5
2.09, 14.47
0.001
3.107
1
0.91
0.91, 4.98
0.078
11.372
1
6.67
1.94, 22.95
0.003
(Variable)=risk factor for depression
(B) Perceived current health status and anxiety levels
Total
Disturbance due to anxiety
Perceived current health status
Pearson chi-square test used
Non-depressed
Depressed
Time
n
%
n
%
n
%
No
1M
156
76.8
119
81.5
37
64.9
(Yes)
1M
47
23.2
27
18.5
20
35.1
No
6M
171
91.0
155
92.3
16
80.0
20.0
(Yes)
6M
17
9.0
13
7.7
4
Good/average
1M
109
50.9
89
58.6
20
32.3
(Poor)
1M
105
49.1
63
41.4
42
67.7
Good/average
6M
155
82.4
146
86.9
9
45.0
(Poor)
6M
33
17.6
22
13.1
11
55.0
χ
2
df
OR
95% CI
p
6.345
1
2.38
1.20, 4.73
0.013
3.267
1
2.98
0.87, 10.23
0.083
12.183
1
2.97
1.59, 5.52
<0.001
21.686
1
8.11
3.02, 21.79
<0.001
(Variable)= risk factor for depression
97
(C) Occurrence of different facial expressions over the past week (binary answer)
Total
Non-depressed
Depressed
Time
n
%
n
%
n
%
χ
1M
133
62.4
103
67.8
30
49.2
2
df
OR
95% CI
p
6.410
1
2.17
1.19, 3.98
0.012
12.792
1
5.13
1.96, 13.47
0.001
9.349
1
--
--
NA
0.487
1
--
--
NA
52.094
1
13.93
6.15, 31.54
<0.001
31.649
1
15.2
4.78, 48.35
<0.001
df
OR
95% CI
p
Displaying of various facial expression over past week
a happy facial expression
Yes
a flat facial expression
an upset/tearing facial expression
(No)
1M
80
37.6
49
32.2
31
50.8
Yes
6M
138
73.4
130
77.4
8
40.0
(No)
6M
50
26.6
38
22.6
12
60.0
No
1M
21
9.9
21
13.8
0
0.0
(Yes)
1M
192
90.1
131
86.2
61
100.0
No
6M
4
2.1
4
2.4
0
0.0
(Yes)
6M
184
97.9
164
97.6
20
100.0
No
1M
111
52.1
103
67.8
8
13.1
(Yes)
1M
102
47.9
49
32.2
53
86.9
No
6M
137
72.9
133
79.2
4
20.0
(Yes)
6M
51
27.1
35
20.8
16
80.0
(D) Occurrence of different emotions during hospitalisation
Total
Non-depressed
Depressed
Time
n
%
n
%
n
%
χ
Reported depression by 1 question
1M
67
31.3
28
18.4
39
62.9
40.516
1
7.51
3.88, 14.5
<0.001
Reported anxiety by 1 question
1M
26
12.1
18
11.8
8
12.9
0.046
1
1.72
0.93, 3.16
0.829
2
Proportion reported ‘Yes’
Reported worry by 1 question
1M
71
33.2
45
29.6
26
41.9
3.020
1
1.11
0.45, 2.68
0.083
Reported panic by 1 question
1M
43
20.1
29
19.1
14
22.6
0.336
1
1.24
0.6, 2.54
0.562
1M
15
7.0
6
3.9
9
14.5
7.547
1
4.13
1.4, 12.16
0.006
Reported agitation by 1 question
Pearson chi-square test used
(Variable)= risk factor for depression
NA- Not applicable; OR was not estimated if cell empty/ row total =0-10
98
(E) Occurrence of different facial expressions over the past week (ordinal scale)
Total
Non-depressed
Depressed
Time
Mean
SD
Mean
SD
Mean
SD
t
df
OR
95% CI
p
1M
1.9
1.0
1.8
1.0
2.3
0.8
-4.224
147
1.86
1.31, 2.62
<0.001
6M
2.0
0.8
1.9
0.8
2.6
0.5
-4.020
186
4.41
1.95, 9.94
<0.001
1M
1.8
0.9
1.8
1.0
2.0
0.8
-1.495
211
1.28
0.92, 1.77
0.137
6M
1.8
0.7
1.8
0.7
1.8
0.6
-0.129
186
1.05
0.51, 2.11
0.897
1M
0.7
0.9
0.4
0.7
1.6
0.9
-8.877
85
5.03
3.19, 7.91
<0.001
6M
0.4
0.6
0.2
0.5
1.4
0.8
-8.556
186
7.84
3.82, 16.07
<0.001
Displaying of various facial expression over past week
a happy facial expression (0-3)
a
a flat facial expression (0-3)
an upset/tearing facial expression(0-3)
Student’s t-test used
a
Reversed coding done
Table 3.21 The association of social support domain factors with DAS at six months
Non-depressed
Depressed
n=168
n=20
Mean
SD
Mean
SD
T
df
OR
95% CI
p
20.8
8.0
17.0
6.5
2.051
186
0.94
0.88, 0.99
0.045
Subtotal score of LSNS on family (0-15)
4.7
3.6
4.7
4.1
0.037
186
1
0.88, 1.13
0.971
Subtotal score of LSNS on relatives (0-15)
5.2
4.6
3
4.0
2.086
186
0.88
0.78, 0.99
0.044
Subtotal score of LSNS on confidant (0-20)
10.8
4.0
9.3
4.1
1.631
186
0.91
0.81, 1.02
0.107
Sum score of Lubben Social Network Scale 6M (0-50)
Student’s t-test used
99
3.2.3.2 Using GDS/CESD as the assessment tool
The factors associated with depression measured by GDS are displayed in Tables
3.22 and those measured by CESD in Table 3.23.
Demographics. The findings were in line with that of DSM. Four factors, as in DSM,
were able to demonstrate a significant relationship with depression: ‘not married’,
female, ‘not living with family’ and ‘length of hospital stay’ (see Table 3.22A).
Cognitive and communicative domain. Ignoring factors with cell count in the row
total with fewer than ten subjects, visual acuity problems remained highly significant
as depicted by the three tools. Not identified by DSM, some other significant risk
factors were revealed by GDS: dysarthria, swallowing and memory problems at one
month. But such significant findings did not persist to the six-month follow-up, when
the frequency of participants having dysarthria or memory problems had dropped
drastically to four and nine respectively (see Table 3.22B). The highly significant
relationship between DAS and mental capacity reflected by the Abbreviated Mental
Test (AMT) was noted by the three tools at the two time points (see Tables 3.18B,
3.22B, 3.23B).
Dependency and somatic symptoms domain. Similar to those assessed by DSM,
self-care functions and some somatic symptoms in this domain were factors associated
with DAS at one month. Scores on the Barthel Activity of Daily Living Index, stomach
pain, joint pain and the total aggregate number of body symptoms (10 types) remained
significant factors at six months (see Table 3.23C), as in line with DSM.
100
Table 3.22 Factors associated with GDS at one and six months
(A) Demographics
Age
Time
OR
95%CI
p
1M
1
0.97, 1.04
0.789
0.002
Gender - Female
1M
2.71
1.45, 5.04
Years of formal education
1M
0.94
0.88, 1.01
0.077
Length of hospital stay
1M
1.05
1.02, 1.07
<0.001
Marital status – Not married
1M
3.09
1.65, 5.78
<0.001
Not living with family
1M
2.28
1.04, 5.03
0.041
Had financial assistance from government
1M
0.84
0.41, 1.71
0.623
Individual logistic regression done; Dependent variable=GDS for Tables A-E
(B) Cognitive and Communicative domains
Sum score Abbreviated Mental Test
Time
OR
95%CI
p
1M
0.64
0.51, 0.81
<0.001
6M
0.57
0.44, 0.74
<0.001
<0.001
Had problems in
Expressive aphasia
1M
4.17
1.95, 8.95
6M
11.214
0.98, 127.87
0.052
global aphasia
1M
8.83
0.90, 86.72
0.062
6M
--
--
NA
Dysarthria
1M
2.59
1.29, 5.18
0.007
6M
1.78
0.17, 17.72
0.622
Swallowing
1M
2.5
1.06, 5.89
0.037
6M
5.74
1.1, 29.94
0.038
visual acuity
1M
3.07
1.49, 6.33
0.002
6M
6.12
1.65, 22.67
0.007
memory
1M
2.11
1.03, 4.34
0.042
6M
2.81
0.66, 11.94
0.160
NA- Not applicable and OR can’t be calculated due to empty cell
(C) Dependency and somatic symptoms domain
Time
OR
95%CI
p
BADL sum score
1M
0.82
0.77, 0.88
<0.001
6M
0.85
0.78, 0.91
<0.001
Modified Rankin Scale for Quality of Life 0 to 5
1M
2.04
1.54, 2.69
<0.001
6M
1.86
1.28, 2.71
0.001
1M
0.61
0.52, 0.72
<0.001
1M
4.59
1.81, 11.61
0.001
6M
1.64
0.42, 6.36
0.471
stomach pain
1M
3.08
1.16, 8.21
0.024
6M
1.06
0.11, 9.36
0.962
non-specific muscle pain
1M
1.15
0.45, 2.94
0.766
6M
2.94
1.25, 6.90
0.013
dizziness
1M
2.39
1.15, 4.98
0.020
6M
2.57
0.90, 7.34
0.078
other specific health problems
1M
1.98
0.95, 4.12
0.068
6M
3.71
1.23, 11.11
0.020
1M
1.54
1.16, 2.04
0.003
6M
1.41
0.94, 2.11
0.092
1M
1.62
1.10, 2.37
0.014
6M
2.77
1.43, 5.33
0.002
1M
1.41
1.15, 1.73
0.001
6M
1.56
1.13, 2.13
0.006
Norton Scale of pressure sore sum score
a
Level of disturbance due to
headache
Total no. of reported pain
Total no. of reported symptoms excluding pains
Total no. of body symptoms
a
This item was not included at 6M questionnaire
101
(D) Emotional domain
Perceived current health status
Worry about current health
a
Time
OR
95%CI
p
1M
2.99
1.57, 5.68
0.001
6M
9.48
3.94, 22.76
<0.001
1M
5.68
2.92, 11.02
<0.001
1M
2.31
1.57, 3.39
<0.001
6M
6.56
3.07, 13.97
<0.001
1M
1.47
1.04, 2.07
0.028
6M
1.69
0.90, 3.12
0.097
1M
3.34
2.26, 4.94
<0.001
Displaying of various facial expression over past week
a happy facial expression (Reversely coded)
a flat facial expression
an upset/tearing facial expression
Disturbance due to anxiety
6M
4.79
2.69, 8.48
<0.001
1M
3.16
1.59, 6.26
0.001
6M
3.34
1.12, 9.88
0.029
1M
5.27
2.42, 11.46
<0.001
Disturbance due to worry over
stroke rehabilitation progress
Disturbance due to
6M
2.97
1.28, 6.82
0.011
financial problem
1M
3.05
1.59, 5.85
0.001
6M
1.76
0.73, 4.2
0.207
bodily pain and discomforts
1M
2.44
1.28, 4.66
0.007
6M
4.50
1.99, 10.17
<0.001
poor family relationship
1M
3.57
1.52, 8.39
0.003
6M
5.39
1.66, 17.41
0.005
losing job/loss of job
1M
0.90
0.31, 2.61
0.852
6M
4.86
1.76, 13.40
0.002
other things
1M
0.62
NA
0.467
6M
0.90
NA
0.861
1M
2.96
1.56, 5.60
0.001
1M
1.51
1.22, 1.88
<0.001
6M
2
1.45, 2.75
<0.001
depression
1M
3.24
1.72, 6.12
<0.001
worry
1M
1.54
0.82, 2.88
0.181
anxiety
1M
2.26
0.97, 5.28
0.058
panic
1M
1.44
0.70, 2.97
0.327
1M
1.93
0.66, 5.70
0.231
disease deterioration
a
Sum of life stressors
Reported emotions during hospitalization
a
agitation
a
This item was not included at 6M questionnaire;
NA- Not applicable and OR can’t be calculated due to empty
cell
(E) Social support domain
Time
OR
95%CI
p
Finding someone to chat
1M
1.34
0.55, 3.23
0.520
Going for a walk
1M
0.57
0.15, 2.13
0.404
Exercising
1M
2.26
0.73, 6.97
0.155
0.001
Usefulness of means to relieve unhappiness before stroke
Sum score of Lubben Social Network Scale (LSNS) 6M
6M
0.9
0.85, 0.95
Subtotal score of LSNS on family
6M
0.97
0.86, 1.07
0.520
Subtotal score of LSNS on relatives
6M
0.89
0.80, 0.98
0.022
Subtotal score of LSNS on confidant
6M
0.83
0.74, 0.91
<0.001
102
Table 3.23 Factors associated with CESD at one and six months
(A) Demographics
Time
OR
95%CI
p
1M
1
0.97, 1.03
0.938
Gender - Female
1M
3.33
1.72, 6.45
<0.001
Years of formal education
1M
0.94
0.87, 1.01
0.080
Length of hospital stay
1M
1.03
1.01, 1.05
0.003
Marital status – Not married
1M
3.43
1.77, 6.64
<0.001
Not living with family
1M
2.49
1.11, 5.59
0.027
Age
Individual logistic regression done; Dependent variable=CESD for Tables A-E
(B) Cognitive and communicative domains
Time
OR
95%CI
p
Sum score Abbreviated Mental Test
1M
0.63
0.49, 0.80
<0.001
6M
0.67
0.51, 0.85
0.002
Expressive aphasia
1M
2.17
0.98, 4.79
0.056
6M
2.22
0.22, 22.24
0.497
Global aphasia
1M
10.93
1.11, 107.65
0.040
6M
--
--
NA
Dysarthria
1M
2.68
1.31, 5.50
0.007
6M
2.22
0.22, 22.24
0.497
Visual acuity
1M
4.75
2.26, 9.99
<0.001
6M
12.55
3.24, 48.5
<0.001
Memory
1M
2.1
1.00, 4.44
0.051
6M
1.94
0.37, 9.90
0.427
NA- Not applicable and OR can’t be calculated due to empty cell
(C) Dependency and somatic symptom domains
Time
OR
95%CI
p
BADL sum score
1M
0.83
0.78, 0.89
<0.001
6M
0.91
0.85, 0.99
0.019
Modified Rankin Scale for Quality of Life 0 to 5
1M
2.21
1.63, 2.99
<0.001
6M
1.86
1.28, 2.71
0.001
1M
0.65
0.56, 0.76
<0.001
1M
3.78
1.50, 9.58
0.005
6M
3.26
0.92, 11.52
0.067
Norton Scale of pressure sore sum score
a
Level of disturbance due to
headache
1M
3.9
1.45, 10.47
0.007
6M
7.27
1.38, 38.30
0.019
1M
2.39
1.18, 4.86
0.016
6M
3.36
1.15, 9.79
0.026
1M
2.12
0.98, 4.57
0.055
6M
2.15
0.71, 6.46
non-specific muscle pain
1M
2.25
6M
0.75
dizziness
1M
2.72
1.29, 5.77
0.009
6M
3.36
1.15, 9.79
0.026
1M
1.66
0.77, 3.58
0.199
6M
4.36
1.45, 13.15
0.009
1M
1.78
6M
2.4
Total no. of reported pain
1M
1.72
6M
1.59
1.05, 2.41
0.030
Total no. of reported symptoms excluding pains
1M
1.68
1.13, 2.50
0.010
6M
3.3
1.65, 6.59
0.001
Total no. of body symptoms
1M
1.51
1.22, 1.88
<0.001
6M
1.74
1.25, 2.43
0.001
stomach pain
joint pain
low back pain
constipation
other specific health problems
a
0.174
0.076
0.620
0.276
0.160
1.28, 2.31
<0.001
This item was not included at 6M questionnaire
103
(D) Emotional domain
Perceived current health status
a
Level of worry about current health (0-3)
Time
OR
95%CI
p
1M
3.85
1.90, 7.80
<0.001
6M
9.65
3.83, 24.28
<0.001
1M
16.5
6.90, 34.99
<0.001
1M
2.01
1.35, 2.96
<0.001
6M
4.67
2.21, 9.84
<0.001
1M
1.42
0.99, 2.04
0.056
6M
2.05
1.03, 4.05
0.038
1M
3.04
2.06, 4.47
<0.001
Displaying of various facial expression over past week
a happy facial expression (0-3) (reversely coded)
a flat facial expression (0-3)
an upset/tearing facial expression (0-3)
6M
3.96
2.23, 7.04
<0.001
1M
3.75
1.85, 7.59
<0.001
6M
4.36
1.44, 13.15
0.009
1M
5.65
2.40, 13.30
<0.001
6M
2.84
1.16, 6.91
0.022
financial problem
1M
2.69
1.37, 5.28
0.004
6M
0.91
0.32, 2.60
0.867
bodily pain and discomforts
1M
2.72
1.39, 5.32
0.003
6M
7.28
2.94, 17.96
<0.001
poor family relationship
1M
1.7
0.68, 4.21
0.253
6M
7.04
2.14, 23.13
losing job/loss of job
1M
0.56
6M
1.88
other things
1M
2.19
Disturbance due to anxiety
Disturbance due to worry over
stroke rehabilitation progress
0.001
0.377
0.3
0.75, 6.36
0.151
6M
3.57
1.35, 9.37
0.01
1M
5.76
2.87, 11.56
<0.001
1M
1.53
1.24, 1.90
<0.001
6M
2
1.45, 2.75
<0.001
depression (0-1)
1M
9.07
4.42, 18.58
<0.001
worry (0-1)
1M
3.37
1.74, 6.53
<0.001
anxiety (0-1)
1M
1.96
0.81, 4.72
0.134
panic (0-1)
1M
1.88
0.90, 3.92
0.095
agitation (0-1)
1M
3.27
1.12, 9.54
0.03
disease deterioration
a
Sum of life stressors
Reported emotions during hospitalization
a
a
This item was not included at 6M questionnaire;
NA- Not applicable and OR can’t be calculated due to
empty cell
(E) Social support domain
Time
OR
95%CI
p
Finding someone to chat
1M
1.56
0.53, 4.51
0.413
Going for a walk
1M
1.69
0.71, 4.07
0.238
Exercising
1M
2.14
0.86, 5.31
0.100
Usefulness of means to relieve unhappiness before stroke 1M
Sum score of Lubben Social Network Scale 6M
6M
0.9
0.84, 0.95
0.001
Subtotal score of LSNS on family
6M
0.87
0.77, 0.98
0.033
Subtotal score of LSNS on Relatives
6M
0.9
0.80, 0.99
0.043
Subtotal score of LSNS on Confidant
6M
0.88
0.79, 0.97
0.017
104
Emotional domain. ‘Worries over stroke rehabilitation’ and ‘level of disturbance
related to body pains and discomfort’ had a persistent impact on DAS, as assessed by
the three tools at one and six months (see Tables 3.20A, 3.22D, 3.23D). Not found in
DSM, the ‘level of disturbance related to financial problems’ was only identified by
GDS (see Table 3.22D) and CESD (see Table 3.23D). But this financial burden did not
have a lasting effect on depression at six months. In addition, the ‘level of disturbance
related to poor family relationships’ was identified by both GDS and CESD at six
months but not by DSM (see Tables 3.22D and 3.23D). The aggregate number of life
stressors also had a significant role in depression disregard the tool used and time of
assessment (see Table 3.22D, and Table 3.23D).
Besides worries, DAS was found to be related to the participants’ perceived
current health status, ‘level of disturbance due to anxiety’ and frequency of displaying
different facial expressions (happy/flat/upset or tearing face) over the past week were
assessed by all three assessment tools; in particular, the inability to present a happy
face and the occurrence of an upset/tearing face were highly significant risk factors
(p<0.001 see Table 3.22D and Table 3.23D). This significant relationship was
sustained at six months. The effect of anxiety on DAS was different with the three
tools. GDS and CESD were able to show a significant relationship between
disturbance due to anxiety and depression at one month (see Tables 3.20B, 3.22D,
3.23D), but the result could not be duplicated at six months or by all three tools. In a
direct assessment of types of emotions displayed during acute hospital admission for
first stroke, only a minority of the participants mentioned depression, worry, panic,
anxiety or agitation (in descending order; see Tables 3.20C, 3.22D, 3.23D).
Social support domain.
The available strategies used by subjects to cope with
unhappiness before stroke had no effect on DAS at one month, whatever the
assessment tools used (see Tables 3.22E, 3.23E). On the other hand, scores on Lubben
Social Network Scale (LSNS) at six months were identified as having a significant
protective effect on depressed subjects (p=0.001, OR=0.90, 95%CI=0.84, 0.95, see
Tables 3.22E, 3.23E). Unlike GDS and CESD, DSM was able to find a weak
association between DAS and LSNS only (p=0.045, OR=0.94, 95%CI=0.88, 0.99, see
105
Table 3.21).
3.2.4 Predictors of DAS
3.2.4.1 Using DSM IV as the assessment tool
The identification of determinants for depression measured by DSM at one
month and six months is presented in this section. The determinants for depression
identified by GDS and CESD will be dealt with subsequently.
One-month factors selected in the regression analysis included (1) bio-anatomical
domain: laterality of lesion, (2) physical domain: Barthel Activity Daily Living,
Modified Rankin Scale for Quality of Life, Norton Scale of Pressure Sore, the number
of pains, reported symptoms and the aggregate number of somatic complaints, (3)
cognitive and communicative domain: Abbreviated Mental Test score, level of
disturbance in visual acuity and memory, (4) emotional domain: level of disturbance
due to anxiety, poor perceived current health status, various worries (about health
status, stroke rehabilitation), displaying a happy/upset or tearing facial expression over
the past week, and the level of disturbance due to body pains and discomfort.
Three mildly significant variables in the bio-anatomical domain were excluded
because there was too much data missing (more than half of the subjects): stroke types,
use of anticoagulants, serum level of high density lipoprotein. The significant
single-item factors in the emotional domain, ‘reported depression/anxiety/worry
during hospitalisation’ were omitted because of their inclusion in GDS and CESD
assessment and the likelihood of skewing the final regression model. At one month,
the determinants for DAS were: ‘displayed an upset/tearing facial expression over the
past week’ (OR=3.11, 95% CI=1.89, 5.09, p<0.001) and ‘level of worry over current
health status’ (OR=2.48, 95% CI=1.52, 4.01; p<0.001) and age (OR=1.06, 95%
CI=1.01, 1.11, p=0.038) as assessed by DSM (see Table 3.25). At the six-month
analysis, two other factors were identified by the model: ‘perceived current health as
bad’, ‘sum score of Abbreviated Mental Test’ on top of ‘displayed an upset/tearing
facial expression over the past week’. In the six-month analysis, two factors were not
106
used as they were not measured at that time: the Norton Scale of Pressure Sore and
‘worry about current health status’.
Table 3.24 Selected variables from DSM assessment for regression analysis (from Tables 3.15-3.19)
Factors associated with depression by DSM IV
p (at 1M)
p (at 6M)
<0.001
0.001
<0.001
<0.001
<0.001
--
Joint pain
0.020
0.006
Dizziness
0.018
0.028
Total no. of reported pain
0.002
NS
Total no. of reported symptoms excluding pains
0.013
0.007
Total no. of body symptoms
0.001
0.008
Abbreviated Mental Test
<0.001
0.001
Problems in visual acuity
0.002
NA
Memory problems
0.043
NA
Physical domain (physiological, bio-anatomical, biochemical)
Barthel Activity Daily Living
Modified Rankin Scale for Quality of Life
Norton Scale of Pressure Sore
b
Pains and somatic complaints
Cognitive and communicative domain
Emotional domain
Disturbance due to anxiety
0.013
NS
Perceived health status - Poor
<0.001
<0.001
<0.001
--
<0.001
<0.001
NS
NS
<0.001
<0.001
Worry about health status
b
Displaying of various facial expression over past week
a happy facial expression (0-3)
a flat facial expression (0-3)
an upset/tearing facial expression (0-3)
Worries over
stroke rehabilitation
<0.001
0.011
body pains and discomfort
<0.001
0.001
NS
0.003
0.001
0.001
--
0.045
poor family relationship
Total number of life stressors
Sum score of Lubben Social Network Scale 6M (0-50)
a
PACI= Partial anterior cerebral infarction;
b
The item was not included at 6M;
c
The item was not included at 1M;
c
TACI= Total anterior cerebral infarction;
NS= Not significant;
NA= Not applicable if cell count was </=10
107
3.2.4.2 Using GDS/CESD as the assessment tool
At one month, the common determinants of DAS assessed by GDS/CESD
(compared with DSM) were: ‘displayed an upset/tearing facial expression over the
past week’ (OR=2.50, 95% CI=1.54, 4.03, p<0.001, see Table 3.26), and ‘level of
worry over current health status’ (OR=3.51, 95% CI=2.03, 6.04, p<0.001, see Table
3.27). Other determinants identified by GDS alone at one month included:
z ‘Sum score of Norton Scale for Pressure Sore’ (OR=0.54, 95% CI=0.38,
0.74, p<0.001)
z ‘Modified Rankin scale for Quality of Life’ (OR=0.44, 95% CI=0.23, 0.81,
p=0.009)
z ‘Length of hospital stay’ (OR=1.03, 95% CI=1.01, 1.06, p=0.034)
z ‘Total number of life stressors’ (OR=1.66, 95% CI=1.22, 2.25, p=0.001,
see Table 3.26)
With CESD at one month, three more independent factors for DAS were
identified on top of the one mentioned above:
z ‘Sum score of Norton Scale for Pressure Sore’ (OR=0.65, 95% CI=0.50,
0.83, p=0.001)
z ‘Level of disturbance over financial problems’ (OR=1.93, 95% CI=1.24,
2.98, p=0.003)
z ‘Level of disturbance due to visual problems’ (OR=2.63, 95% CI=1.37,
5.02, p=0.003, see Table 3.27)
108
Table 3.25 Determinants of DAS assessed by DSM IV
1M
6M
OR
95%CI
p
OR
95%CI
p
Displayed an upset/tearing face over the past week
3.11
1.89, 5.09
<0.001
13.74
2.55, 73.93
0.002
Level of worry over current health status
2.48
1.52, 4.01
<0.001
Age
1.06
1.01, 1.11
0.038
Perceived current health status as bad
11.34
2.85, 45.01
0.001
Sum score of Abbreviated Mental Test
0.45
0.26, 0.74
0.002
Logistic regression was used.
The model has been adjusted for gender, age, length of hospital stay, marital status and living arrangement
Table 3.26 Determinants of DAS assessed by GDS
1M
6M
OR
95%CI
p
OR
95%CI
p
Displayed an upset/tearing face over the past week
2.50
1.54, 4.03
<0.001
3.55
1.51, 8.32
0.004
Sum score in Norton Scale for Pressure Sore
0.54
0.38, 0.74
<0.001
Total number of life stressors
1.66
1.22, 2.25
0.001
Modified Rankin Scale for Quality of Life
0.44
0.23, 0.81
0.009
Length of hospital stay
1.03
1.01, 1.06
0.034
Sum score of Abbreviated Mental Test
0.55
0.36, 0.81
0.003
Perceived current health status as bad
2.47
1.05, 5.79
0.037
Level of disturbance due to losing job
2.44
1.07, 5.53
0.033
Displayed a happy face over the past week (reversely coded)
2.96
1.04, 8.35
0.041
Logistic regression was used.
The model has been adjusted for gender, age, length of hospital stay, marital status and living arrangement
109
Table 3.27 Determinants of DAS assessed by CESD
1M
6M
OR
95%CI
p
OR
95%CI
p
Level of worry over current health status
3.51
2.03, 6.04
<0.001
Sum score in Norton Scale for Pressure Sore
0.65
0.50, 0.83
0.001
Level of disturbance due to financial problems
1.93
1.24, 2.98
0.003
Level of disturbance due to visual problems
2.63
1.37, 5.02
0.003
Perceived current health status as bad
5.72
2.18, 14.97
<0.001
Gender – Female
0.25
0.06, 0.96
0.045
Total number of life stressors
1.66
1.08, 2.54
0.020
Sum score of Lubben Social Network Scale
0.89
0.81, 0.97
0.016
Displayed an upset/tearing face over the past week
2.65
1.20, 5.81
0.015
Logistic regression was used.
The model has been adjusted for gender, age, length of hospital stay, marital status and living arrangement
110
At six months, the determinants identified by GDS were all identified by DSM in
the regression analysis: ‘displayed an upset/tearing face over the past week’,
‘perceived current health status as bad’ and ‘sum score on Abbreviated Mental Test’.
CESD also captured the first two but not the third (see Tables 3.25-3.27). Besides the
common ones, GDS/CESD also identified the following factors in explaining DAS:
GDS
z
‘Level of disturbance due to losing job’ (OR=2.44, 95% CI=1.07, 5.53,
p=0.033)
z
‘Displayed a happy face over the past week’ in reversed coding (OR=2.96,
95% CI=1.04, 8.35, p=0.041, see Table 3.26)
CESD
z
‘Perceived current health status as bad’ (OR=5.72, CI=2.18, 14.97,
p<0.001)
z
‘Total number of life stressors’ (OR=1.66, 95% CI=1.08, 2.54, p=0.020)
z
‘Sum score on Lubben Social Network Scale’ (OR=0.89, CI=0.81, 0.97,
p=0.016)
z
‘Displayed an upset/tearing face over the past week ‘ (OR=2.65, 95%
CI=1.20, 5.81, p=0.015)
z
Gender - female (OR=0.25, CI=0.06, 0.96, p=0.045, Table 3.27)
111
Predictors for depression scores. This section explores factors that helped to predict
depression at six months. The predictors for the depression scores at six months and
those that can explain the change in depression score are shown in Tables 3.28 and
3.29 respectively. The predictors from three domains - emotional, cognitive and
dependency and somatic symptom domains at one month - jointly explained 44%
variance in depression scores measured by GDS at six months (see Table 3.28).
Factors in the emotional domain were: ‘days in the last week with an upset/tearing
facial expression’, ‘level of disturbance due to worry about disease deterioration’, and
‘level of worry about current health status’. ‘Level of disturbance due to memory’ and
AMT scores were factors in the cognitive domain. In the dependency and somatic
symptoms domain, three factors were identified: ‘level of disturbance due to other
problems’, ‘length of hospital stay’, and ‘level of disturbance due to constipation’ (see
Table 3.28).
The predictors accounting for the change in depression score assessed by GDS
closely resembled those in the previous paragraph. The factors from the same three
domains were able to explain 60% of the variance in the change of depression score
from the acute to chronic phase. Additional predictors included the Barthel Activity of
Daily Living score, and the ‘level of disturbance due to financial problems’ in the
dependency and somatic symptoms domain; and ‘perceived current health status’
instead of ‘level of worry about current health status’ in the cognitive domain. Age
instead of ‘length of hospital stay’ was also found (see Tables 3.28, 3.29).
112
Table 3.28 Predictors of depression scores (by GDS) at six months
Estimated
effect
95%CI
p
Cognitive domain
Level of disturbance due to memory problems at 1M
0.76
0.04, 1.48
0.039
AMT score at 1M
-0.61
-0.98, -0.25
0.001
Dependency and somatic symptoms domain
Length of hospital stay
0.05
0.02, 0.07
0.001
Level of disturbance due to other problems at 1M
-1.12
-1.80, -0.43
0.002
Level of disturbance due to constipation at 1M
1.38
0.66, 2.10
<0.001
Emotional domain
Days in the last week with an upset/tearing facial expression at 1M
0.65
0.11, 1.19
0.018
Level of disturbance due to worry about disease deterioration at 1M
0.52
0.15, 0.89
0.006
Level of worry about current health status at 1M
0.56
0.08, 1.05
0.023
2
R = 0.44;
Model was adjusted for age, gender, length of hospital stay
Table 3.29 Predictors accounting for the change in depression scores (by GDS) from one to six months
Estimated
effect
95%CI
p
0.02, 0.17
0.008
Demographics
Age
0.10
Cognitive domain
Level of disturbance due to memory problems at 1M
1.15
0.56, 1.72
<0.001
AMT score at 1M
-2.07
-3.18, -0.95
<0.001
Dependency and somatic symptoms domain
BADL score at 1M
0.23
0.08, 0.37
0.002
Level of disturbance due to other problems at 1M
1.45
0.36, 2.53
0.009
Level of disturbance due to constipation at 1M
-2.30
-3.43, -1.16
<0.001
Level of disturbance due to financial problem at 1M
-0.86
-1.5, -0.2
0.010
Days in the last week with an upset/tearing facial expression at 1M
-1.54
-2.39, -0.67
0.001
Level of disturbance due to worry about disease deterioration at 1M
-0.89
-1.49, -0.29
0.004
Perceived current health status at 1M
-1.26
-2.11, -0.41
0.004
Emotional domain
2
R = 0.60;
Model was adjusted for age, gender, length of hospital stay
3.2.5 Health literacy of depression
Of a total of 214 patients participating in the study, 86 (40.2 %) described
depression in their own words. This group was hereafter referred as the ‘depression
literate’ group. The remainder of the patients, referred to as the ‘depression illiterate’
group, explicitly remarked their lack of knowledge or understanding of the term
‘depression’ in Cantonese, which is the prevailing Chinese language. For both literate
and illiterate groups, about 30% were identified as clinically depressed using DSM IV
criteria (see Table 3.30). There was no significant difference in the age and length of
hospital stay between the two groups. Only about one third of the respondents in either
group expressed an interest in knowing more about depression. But participants in the
depression literate group had significantly more formal education (mean=6.1 years)
113
than their counterparts (mean=4.4 years, t=-2.449, p=0.015, see Table 3.30).
Core emerging themes of depression interpretation fell into affective, behavioural
and cognitive domains. Sixty participants in the depression literate group (69.8%)
described depression using terms in the affective domain, 25 (29.1%) in the
behavioural and 39 (45.3%) in the cognitive domains. Forty participants used
descriptors in dual domains: affective and behavioural (20.9%), affective and
cognitive (19.8%), behavioural and cognitive (5.8%, see Table 3.31).
Table 3.30 Comparing demographic and depression between of depression literate and illiterate groups
Depression illiterate
Depression literate
(n=128)
(n=86)
Mean
SD
Mean
SD
t
df
p
Age
72.4
9.7
72.2
9.0
0.158
212
0.874
Days of hospital stay
17.7
15.4
16.2
15.3
0.702
212
0.483
Years of formal education
4.4
4.8
6.1
5.2
-2.449
211
0.015
Gender – Male, n (%)
80
(62.5)
55
(64.0)
Had depression – by DSMIV, n (%)
36
(28.1)
26
(30.2)
Paired sample t-test was done for age, days of hospital stay and years of formal education
Table 3.31 Domains of emerging themes on depression interpretation by 86 participants
Domains of descriptors for depression
Affective
n
%
60
69.8
Behavioral
25
29.1
Cognitive
39
45.3
Affective and behavioral
18
20.9
Affective and cognitive
17
19.8
Behavioral and cognitive
5
5.8
3.2.5.1 Descriptors in affective domain
‘Unhappy’.
There were 60 illustrations depicting the affective perspective on
‘depression’, ‘unhappy’, ‘worries’, ‘boredom’, ‘keeping things to oneself’, and these
were core mood-related descriptors. The most frequent word used by participants was
‘unhappy’, which was quoted 20 times, among which six also included an intensifier,
such as: ‘very unhappy’ [sc142 F/66-0] [sc184 F/56-0] [sc238 F/71-0] [sc302 M/66-6]
or ‘always unhappy or sad’ [sc81 F/69-6] [sc189 M/79-0] [sc295 M/51-15] [sc293
114
M/71-1].* Some participants suggested that the unhappiness was generated by the
sufferers themselves: ‘Unhappy things locked in one’s heart cause illness’ [sc164
M/78-5]; ‘feeling unhappy by oneself; one confines oneself but one doesn't feel
comfortable’ [sc141 F/72-2]. Five added ‘sulkiness/upset’ alongside an unhappy mood
[sc170 M/67-1] [sc193 F/79-12] [sc219 M/76-15] [sc276 M/78-6] [sc293 M/71-1].
*[scXXX=subject code no; M=male; F=female, followed by age; the last figure
following a hyphen=years of formal education]
‘Burying things in one’s heart’. Altogether 21 scripts mentioned a portrait of the
depressed subjects as ‘burying things within oneself’. What were they burying? Five
suggested ‘unhappiness’, and four problems they wanted to conceal; three mentioned
‘worries’. Quite a few respondents (n=9) did not specify what exactly the
issue/problem was that the depressed people had concealed. This was particularly the
case in women with a low level of formal education (0-4 years) - 4 out of 5 female
respondents (see Table 3.32).
‘Worry/anxiety’ and ‘bored/gloomy’. ‘Worry/anxiety’ and ‘boredom’ were another
two common descriptions of depression. Six had used worries alone to interpret
‘depression’: [sc101 M/83-4] [sc102 M/72-5] [sc132 M/75-6] [sc145 M/70-16] [sc226
M/84-6] [sc240 M/65-12]. Another six added the intensity and frequency of worries:
‘worry too much’ [sc185 M/84-9] [sc289 M/69-2] [sc302 M/66-5], ‘always worry’
[sc132 M/75-6] [sc306 M/63-6] or ‘worry about everything’ [sc263 F/70-10]. Eight
scripts mentioned boredom, three separately with others occurring in combination
and/or specifying the intensity of the mood [sc88 M/62-15] [sc114 M/64-10] [sc140
F/87-2] [sc251 F/68-12] [sc263 F/70-10].
‘Fidgeting/irritated’. This was mentioned six times to interpret depression. Such
emotion, however, was seen to follow other descriptors rather than being posed at the
beginning of the illustration. Examples were as follows:
z ‘Got to take medicine; always in a fidget, always feel sad’ [sc81 F/69-6]
z ‘Unhappy; in a fidget’ [sc105 M/64-15]
z ‘Patient very irritated; feeling sick in different parts of the body’ [sc124
115
M/75-1]
z ‘Confining one’s problems to oneself; not speaking to others; irritated’
[sc144 F/87-0]
z ‘Don't feel at ease all day; always in a fidget and always blaming oneself
for one’s bad temper’ [sc231 M/69-12]
z ‘Feeling bored and throwing oneself into a fidget’ [sc278 M/81-20]
Non-specific descriptors. Some subjects were unable to describe depression
specifically, as shown by the use of vague terms such as ‘in a bad mood’ [sc89 F/68-0]
[sc269 F/72-12], ‘a heavy heart’ [sc166 M/54-6] [sc220 M/83-10] [sc267 M/83-6],
‘feeling uncomfortable’ [sc118 M/73-4] [sc141 F/72-2] [sc219 M/76-15] [sc227
F/81-4], ‘don't feel at ease all day’ [sc231 M/69-12], ‘want to die’ [sc238 F/71-0]
[sc282 F/80-15], ‘feeling lonely’ [sc284 F/81-0], ‘desire for something’ [sc296 F/71-2],
‘neurosis’ [sc300 F/71-5], ‘it's something to do with one's personality’ [sc88 M/62-15]
[sc251 F/68-12], ‘always fail to get what one wants’ [sc84 M/88-4], ‘feeling useless’
[sc112 M/78-14]. This might also reflect the limited literacy regarding depression even
in the ‘depression literate’ group.
116
Table 3.32 Analysis of ‘burying things in oneself and concealing from others’ under affective domain
Unhappiness (5)
feeling unhappy by self; one confines oneself but one doesn't feel comfortable [sc141 F/72-2]*
the unhappy things that locked in one’s heart cause illness [sc164 M/78-5]
keep silent; upset; one is not happy and won't tell anyone; wear a sad face [sc193 F/79-12]
unhappy; don't know to whom one can talk about things that buried in one's heart; don't even want to talk [sc228
F/64-9]
in a bad mood; bury the unhappiness in one’s heart [sc269 F/72-12]
Problems (4)
it's one's own problem; one just confines oneself indoors; the problem could have been solved, but one retreats
to one's own pyramid and get oneself into a dead end; so nothing is solved [sc87 M/59-12]
there are entangled knots in one’s heart; there are personal problems which one doesn't want to spread out, so
garner them up; autism; self-torture [sc88 M/62-15]
wont' tell anybody else even when one has a problem [sc142 F/69-0]
for problems that one can't solve oneself, one would bury them in one’s heart; won't tell anybody else [sc156
M/64-7]
Worries (3)
always worry and bury them in one's heart without speaking them out [sc132 M/75-6]
worry and take things too hard, just like suffering from autism [sc240 M/65-12]
worry too much and conceal them [sc289 M/69-2]
Non-specific (9)
wont' tell anything to others; bury things in one’s heart; keep silent [sc143 F/76-2]
confine things to myself; won't speak to others [sc144 F/87-0]
won't tell anybody about the things concealed in one’s heart [sc166 M/54-6]
bury in one’s heart and won't speak a word [sc218 F/72-0]
sulky; the pains can only be tasted by oneself; feeling uncomfortable and would commit suicide [sc219 M/76-15]
bury something in one's heart and feel uncomfortable. [sc227 F/81-4]
bury things in one's heart and feel unhappy [sc231 M/69-12]
always weep and bury things in one's heart [sc246 M/74-1]
feel melancholy to the extreme; confine oneself; want to die [sc282 F/80-15]
*[scXXX=subject code no; M=male; F=female, followed by age; the last figure following a hyphen=years of formal
education]
117
3.2.5.2 Descriptors in behavioural domain
Among the three domains, the behavioural was the least elaborated. Some
showed that depressed people would display obvious avoidance behaviour such as
‘confining oneself’ (4); decreased or no verbalization, avoiding people - family
members, other people or a medical doctor (13). Other behavioural cues, as used in
DSM IV indicating depression, were only mentioned once or twice (8), for example,
‘cry’ (2) [sc246 M/74-1] [sc283 M/64-12], ‘wear a sad face’ (2) [sc193 F/79-12]
[sc283 M/64-12], ‘can't do lots of things’ [sc90 M/89-6], ‘very lazy in taking care of
myself’ [sc231 M/69-12], ‘displaying bad temper’ [sc276 M/78-6], ‘can't sleep’ [sc306
M/63-6].
‘Keeping to oneself’
z problems not resolvable, kept to self [sc156 M/64-7]
z hide oneself away [sc172 M/64-4]
z keep to one's own heart [sc246 M/74-1]
z keeping to oneself [sc250 M/52-6]
‘Don't/can't let go’
z [sc172 M/64-4], [sc175 M/67-12], [sc186 M/57-3], [sc189 M/79-0], [sc276
M/78-6], [sc297 M/78-6], [sc303 M/90-2]
z [sc153 F/74-0], [sc285 F/91-4]
‘Unwilling to talk to people (Dr/family/others)’
z don’t share with family [sc213 M/62-10] or others [sc142 F/69-0],
z [sc193 F/79-12], [sc250 M/52-6]
z don't let people know [sc156 M/64-7]
z don’t meet people [sc186 M/57-3]
118
z don't see doctor about diseases [sc213 M/61-10]
‘Other behavioural descriptors’
Six other behavioural descriptors identified were: ‘cry’ (2) [sc246 M/74-1];
[sc283 M/64-12], ‘with a sad mouth and face’ (2) [sc283 M/64-12]; [sc193 F/79-12],
‘being uncomfortable’ (1) [sc118 M/73-4], ‘neurosis’ (1) [sc300 F/71-5], ‘display
temper’ (1) [sc276 M/78-6], ‘can't perform what one wishes’ (1) [sc90 M/89-6].
3.2.5.3 Descriptors in cognitive domain
Descriptors in the cognitive domain were the second most commonly cited by the
interviewees. To delineate the nature and content of their thoughts, specific and
non-specific descriptors could be identified, with some elaborated according to the
intensity of these thoughts (see Table 3.33). It was the altered thinking pattern of
‘taking things too hard’ that was quoted most frequently (8). Respondents also cited
‘can’t take things in a relaxed manner’ (4) or ‘can’t understand how things go’ (4).
Four subjects noted that such an emotional state could be a disease (see Table 3.33).
Other specific descriptors included the following (6): ‘to split hairs, think about things
that are hard or bad’ [sc139 M/62-15], ‘the world is grey and there is no future’ [sc134
M/60-12], ‘always deep in thought, brooding over something bad’ [sc138 F/79-0],
‘negative things’ [sc301 M/71-0], ‘thinking from one single perspective’ (2) [sc118
M/73-4] [sc146 F/67-0]. Three people related their cognition to self-worth, and ten
emphasised the intense frequency of such patterns of thought (see Table 3.33).
119
Table 3.33 Analysis of ‘nature and content of thoughts’ under cognitive domain
Specific descriptors (6)
always think in one single perspective [sc118 M/73-4] [sc146 F/67-0]
my world is grey and there is no future [sc134 M/60-12]
always in thought, brood over something bad [sc138 F/79-0]
to split hairs, think about things that are hard or bad [sc139 M/62-15]
think of negative things [sc301 M/71-0]
Related to disease (4)
this is a kind of morbidity; a kind of psychological problem [sc87 M/59-12]
when one is introverted; one will be prone to this illness [sc251 F/68-12]
mental disorder; but don't know what it is [sc272 F/57-5]
the unhappy things locked in my heart bring me this illness [sc164 M/78-5]
Related to self capacity/worth (3)
always assume that neither this nor that can be done [sc106 M/78-0]
thinking that one is useless [sc112 M/78-14] [sc235 F/76-0]
Frequency and intensity of thinking (10)
Always (5)
[sc106 M/78-0], [sc138 F/79-0], [sc179 M/64-3], [sc233 M/66-1], [sc301 M/71-0]
Frequently (5)
think too much [sc146 F/67-0], [sc163 M/85-4], [sc248 F/72-0], [sc285 F/91-4]
lots to think [sc303 M90-2]
Non-specific (4)
think about things that are not relevant, and find that everything is not to one’s liking [sc165 M/77-7]
can't understand how things go and can't be optimistic [sc220 M/83-10]
think things round and round [sc248 F/72-0]
think a lot [sc250 M/52-6]
120
3.3 Clinical outcomes
3.3.1 Post-stroke quality of life
The mean score in quality of life significantly improved, from 2.36 (SD=1.30) to
2.14 (SD=1.16, 95%CI=0.08, 0.36, p=0.002, see Table 3.8A), at six months. About
two-thirds of the subjects reported same or lowered quality of life when comparing the
six-month and one-month scores (see Figure 3.2). To identify the predictors of
post-stroke quality of life (PSQoL) at six months, five respective domain factors at one
month were included in the regression model. Quality of life at six months can be
predicted by three factors at one month: the Barthel Index of Activities of Daily
Living, the score on the Abbreviated Mental Test and level of worry about current
health, together with the length of hospital stay (see Table 3.34). A model with four
factors falling into three domains was able to explain 55% of the variance in the
PSQoL score at six months.
121
Table 3.34 Predictors of quality of life at six months
Predictors (n=178)
Estimated effects
95% CI
p
Length of hospital stay (days)
0.011
0.001, 0.022
0.034
BADL scores at 1M
-0.121
-0.152, -0.089
<0.001
AMT scores at 1M
-0.114
-0.213, -0.016
0.023
Level of worry about current health at 1M
0.126
0.002, 0.25
0.046
2
R =0.553; Stepwise regression analysis undertaken;
Model was adjusted for age, gender and length of hospital stay;
Factors entered into the regression model:
Number of pains; Number of body symptoms; Barthel Index of Activities of Daily Living (BADL)
Level of disturbance due to expressive aphasia; Level of disturbance due to dysarthria;
Level of disturbance due to swallowing; Level of disturbance due to visual acuity
Level of disturbance due to hearing; Level of disturbance due to memory;
Level of worry about current health; Level of disturbance due to worry about stroke rehabilitation progress
Level of disturbance due to anxiety; Level of disturbance due to financial problem
Score of Abbreviated Mental Test (AMT); Score of Geriatric Depression Scale (GDS)
Figure 3.2 Changes in Quality of life score from one month to six months
50.0%
N=82
43.3%
N=69
36.7%
Percent
40.0%
30.0%
N=30
20.0%
20.0%
10.0%
0.0%
Deteriorated
No change
Improved
Change in QoL scores
122
3.3.2 Institutionalisation and mortality
Institutionalisation referred to admissions to any healthcare settings away from
home. Thus admissions to hospital or old-age home for more nursing care resulting
from stroke were taken into account. In this study sample, five participants had been
living at old-age home before stroke. At six months, four non-depressed participants
(2.4%, 4 out of 152) had recurrent stroke as recorded in their medical notes (see Table
3.35B). 30 participants were readmitted to hospital with other medical conditions.
Depressed subjects had significantly more repeated hospitalisation (45% vs. 12.5%
among the non-depressed, p<0.001, see Table 3.35B). More depressed participants
were admitted to nursing home or care and attention home on discharge from hospital
after first stroke onset (22.6%), compared with only 6.6% of the non-depressed
(p=0.001). This pattern disappeared at six months. The total number of
institutionalisations was reduced from 24 at one month to 20 at six months (see Table
3.35B).
Regarding mortality at six months, altogether, 11 and 2 participants had passed
away after the one-month and six-month interviews. A brief comparison of clinical
status and outcome between dropouts (n=26) and participants (n=188) who continued
to take part in this study after one-month interview was made (see Table 3.36). There
was no significant difference between the dropouts and participants remaining in the
study after the one-month interview in terms of gender, depression, psychiatric
consultation, use of anti-depressant, hospitalisation and admission rates to old-age
home, except in the case of mortality. 35% (9 out of 26) among the dropouts passed
away after one month while only 2.1% (4 out of 192) among the remainder. There was
no association between mortality and depression identified at two time points but the
mortality among the depressed subjects was higher (8.1%, 5 out of 62; vs. 3.9%, 6 out
of 152 among the non-depressed, not shown in Table).
123
3.3.3 DAS interventions in formal healthcare
3.3.3.1 Use of antidepressants
Retrieving the medical records and discharge summaries of all the participants at
one month, it was found that only 11.3% among the depressed had started to use
antidepressants (7 out of 62, see Table 3.35A). As there was a time lapse between
record review and one-month follow-up, the prescription data was only available by
retrospective checking. In fact most of the prescription did not occur at one-month but
three months later. The number of patients taking antidepressants was found to
increase to 11 after six months (see Table 3.35B). But 10 of these were in the
non-depressed group, leaving only one identified as depressed and still were being put
on antidepressants. Very few patients were given hypnotics and anxiolytic at both time
points (see Tables 3.35A, B). All the prescriptions were documented in the medical
records except one patient. This patient obtained antidepressants from a general
practitioner and was noted by the interviewer during the follow-up home visit.
3.3.3.2 Use of mental healthcare resources
As identified by the record review after the one-month interview, 11 out of 62
depressed participants (17.7%) had their emotional status (with terms like
‘upset/tearing’, ‘cry’…) documented. Only three depressed subjects were referred to
medical social workers (4.8%, 3 out of 62). Disregard depressive status, only a total of
ten consulted the psychiatrist (4.7%, 10 out of 214). Seven were identified as
depressed while three were not (see Table 3.35A). At six months, formal consultations
with a psychiatrist increased to 16 (8.5%, 16 out of 188). They all belonged to the
non-depressed group, as identified by the DSM IV criteria. 44 patients were invited to
meet a psychogeriatrician after the six-month follow-up as set in the study protocol. 13
came from the depressed group (65.0%) and 31 from the non-depressed (18.5%, see
Table 3.35B). Seven depressed subjects refused the invitation to consultation because
of immobility or resistance from relatives. All these 20 depressed subjects were not
under formal mental healthcare.
124
Table 3.35 Treatment and clinical outcomes of the participants
(A) At one-month follow-up
Total
Nondepressed
Depressed
n
%
n
%
n
%
χ
No
204
95.3
149
98
55
88.7
Yes
10
4.7
3
2
7
11.3
No psychoactive drug
199
93.0
147
96.7
52
83.9
Used anti-depressant
9
4.2
2
1.3
7
11.3
2
df
p
8.581
1
0.003
13.298
3
0.004
7.459
1
0.006
2.558
1
0.110
11.324
1
0.001
At one month
Consulted psychogeriatrian or psychiatrist
Used of medication
Has used anxiolytic
3
1.4
1
0.7
2
3.2
Has used hypnotics
3
1.4
2
1.3
1
1.6
No
211
98.6
152
100
59
95.2
Yes
3
1.4
0
0
3
4.8
Met MSW
Cried during interview
No
188
87.9
137
90.1
51
82.3
Yes
26
12.1
15
9.9
11
17.7
No
190
88.8
142
93.4
48
77.4
Yes
24
11.2
10
6.6
14
22.6
Lived at old age home
125
(B) At six-month follow-up
Total
n
Nondepressed
%
n
%
Depressed
n
%
χ
2
df
p
22.049
2
<0.001
3.310
2
0.191
20.169
1
<0.001
3.295
1
0.070
0.487
1
0.485
14.076
1
<0.001
2.063
1
0.151
At six months
Consult psychogeriatrian or psychiatrist
No
128
68.1
121
72
7
35.0
Yes by invitation
44
23.4
31
18.5
13
65.0
Yes by identified by Doctor/self referred
16
8.5
16
9.5
0
0.0
No
175
93.1
157
93.5
18
90.0
Yes
11
5.9
10
6.0
1
5.0
Only hypnotics
2
1.1
1
0.6
1
5.0
No
172
91.5
159
94.6
13
65.0
Yes
16
8.5
9
5.4
7
35.0
No
186
98.9
167
99.4
19
95.0
Yes
2
1.1
1
0.6
1
5.0
No
184
97.9
164
97.6
20
100.0
Yes
4
2.1
4
2.4
0
0.0
Used of anti-depressant
Cried during interview at 6M
Had passed away
Hospitalized due to recurrent stroke
Hospitalized due to other medical conditions
No
158
84
147
87.5
11
55.0
Yes
30
16
21
12.5
9
45.0
Lived at old age home
No
168
89.4
152
90.5
16
80.0
Yes
20
10.6
16
9.5
4
20.0
126
Table 3.36 Comparison of clinical status and outcome between dropouts and persistent participants
Participants
Total
remained
Dropouts
N
%
n
%
n
%
χ
Male
135
63.1
118
62.8
17
65.4
Female
79
36.9
70
37.2
9
34.6
2
df
p
0.067
1
0.795
0.5
1
0.480
0.003
1
0.960
0.064
1
0.799
52.733
1
<0.001
2.595
1
0.107
0.342
1
0.559
0.517
1
0.472
Gender
Identified depressed by DSM at one month
No
152
71
132
70.2
20
76.9
Yes
62
29
56
29.8
6
23.1
No
197
92.1
173
92.0
24
92.3
Yes
17
7.9
15
8.0
2
7.7
No
200
93.5
176
93.6
24
92.3
Yes
14
6.5
12
6.4
2
7.7
No
203
94.9
186
98.9
17
65.4
Yes
11
5.1
2
1.1
9
34.6
No
208
97.2
184
97.9
24
92.3
Yes
6
2.8
4
2.1
2
7.7
Consulted psychiatrist (Dr/Self referral)
Used anti-depressant
Had passed away
Hospitalized due to recurrent stroke
Hospitalized due to other medical
conditions
No
181
84.6
158
84
23
88.5
Yes
33
15.4
30
16
3
11.5
No
190
88.8
168
89.4
22
84.6
Yes
24
11.2
20
10.6
4
15.4
Lived at Old Age Home after stroke
episode
Pearson chi-square test was used.
127
Chapter 4 Discussion
4.1
Incidence of DAS at one month and six months (130)
4.1.1
Comparing the incidence with published Chinese samples (131)
4.1.2
Comparing the incidence with published Caucasian samples (132)
4.1.3
The trend of DAS at six months and after (133)
4.2
Risk factors and predictors of DAS (135)
4.2.1
Bio-anatomical domain (136)
4.2.2
Cognitive and communicative domains (137)
4.2.3
Dependency and somatic symptoms domain (139)
4.2.4
Emotional domain (140)
4.2.5
Family and social support domain (143)
4.2.6
Demographic and other factors (144)
4.3
DAS interventions (146)
4.3.1
Consequence of inattention to DAS (146)
4.3.2
Primary preventive measures (148)
4.3.2.1
Recognition of DAS - depression literacy (149)
4.3.2.2
Early detection of DAS – use of smiley diagrams (151)
4.3.3
Treatment modalities of DAS (154)
4.3.3.1
Pharmacological treatment (154)
4.3.3.2
Non-pharmacological treatment (155)
4.4
Limitations of study (157)
4.4.1
Threats to internal validity (157)
4.4.1.1
Selection bias (157)
4.4.1.2
Study design (159)
4.4.1.3
History (160)
4.4.1.4
Maturation (161)
4.4.1.5
Mortality (162)
4.4.2
Threats to external validity (162)
4.4.2.1
Sample representativeness (162)
4.4.2.2
Reliability of reporting ‘depression’ in Chinese stroke patients in
Hong Kong hospitals (163)
4.4.2.3
Measurement effects (164)
128
4.5
Recommendations for future research, practice and education (167)
4.5.1
Future research (167)
4.5.1.1
Incidence studies (167)
4.5.1.2
Bio-anatomical or biochemical research (169)
4.5.1.3
Depression literacy studies (169)
4.5.1.4
Detecting depression (170)
4.5.2
Future practice and education (171)
129
This chapter will discuss the major findings of the study in order to answer the
research questions set out at the end of chapter one. The objectives of this section are:
(1) to identify the incidence of DAS at one month and six months among older patients,
(2) to analyse the risk factors and predictors of DAS, (3) to discuss the management of
DAS according to this study result and those in the literature, (4) to evaluate the
limitations of current study and (5) to recommend directions for future research,
practice and education.
4.1 Incidence of DAS at one month and six months
The incidence of DAS estimated by DSM IV at one month is only slightly higher
(29%) than the figure in a recent publication (24%) (data published already, see
Publications). But the incidence of DAS produced by GDS and CESD for the same
sample is lower, with estimates of 27% and 22% respectively. If minor depression is
included, the incidence rises to 46.3% (99 out of 214 using DSM), 52.3% (GDS) and
34.6% (CESD). The incidence at six months drops to 11%, 16% and 13% as identified
by DSM IV, GDS and CESD respectively. In this study, we have classified those
treated with antidepressant and/or having consultation with psychiatrists under
non-depressed group according to the DSMIV assessment. If taking those diagnosed
depressed (with or without oral antidepressant treatment at 6M) into consideration and
categorized under depression group, the cumulative incidence at six months will be
16.0%. In any case, the data reflects the fact that DAS is a significant healthcare
problem under-reported, under-recongized and therefore under-addressed and
under-funded
130
Table 4.1 Comparing the incidence findings with published studies
By DSM IV
Caucasian
Chinese
At one month,
Major Depression only (incidence=29%)
Comparable incidence
32% - DeCoster 200
Our incidence is lower
Our incidence is higher
31%- Liu et al. 2003
12-16%
Eriksson 2004
21.6% - Aben 2003
16% - Tang et al. 2005
21%- Chiu 2005
14%- Morris et al. 1990
5.6% - Berg et al. 2001
Major Depression+ Minor Depression
(incidence=46%)
Comparable incidence
25-50% - NIMH 2002
Our incidence is lower
Our incidence is higher
62% - Fuh et al. 1997
33% -Weimar et al. 2002
32% -Morris et al. 1990
At six months,
Major Depression only (incidence=11%)
Comparable incidence
28% -Beekman et al. 1998
Our incidence is lower
30% - Aben et al. 2003
16% - Kauhanen et al. 1999
15%- Burvill et al. 1995
4.1.1 Comparing the incidence with published Chinese samples
As far as Chinese populations are concerned, several studies have been produced.
Three are on Taiwanese subjects (Fuh et al. 1997, Kao 2000, Chiu et al. 2005), one was
carried out in mainland China (Liu et al. 2003) and two are from Hong Kong (Tang et
al. 2005). The incidence ranges from 13%, from rural samples (Chiu et al. 2005) to
31% (Liu et al. 2003). Three studies adopt the GDS Chinese version as the screening
tool (Chiu et al. 2005; Tang et al. 2005). The brevity of this research history on DAS in
local settings justifies more vigorous research to address inconclusive questions,
controversy and identifiable gaps, which are elaborated in the different sections of this
chapter.
131
4.1.2 Comparing the incidence with published Caucasian samples
There are growing studies on DAS abroad but the comparison of these results
with this study is pragmatically challenging as discussed. Alternatively, studies with
similar study design and type of stroke patients, and which have been undertaken
within six months after first-ever stroke, are selected for crude comparison and
discussion. The findings of incidence/accumulative incidence in ascending order are
summarised as follows (see also Table 4.1):
<10%: 9% and 16% major depression at 3 and 12 months after the stroke based
on DSM-III-R criteria (Kauhanen et al. 1999);
10-20%: by the use of CESD and DSM-IV, 20% identified as depressed on
admission (Cassidy et al. 2004);
21-30%: 23% in the Perth Community Stroke Study four months after stroke
using DSM III (Burvill et al. 1995); two studies reported 27%, one with CESD
(Beekman et al. 1998; Berg et al. 2001) and the later one measuring depression at 2
weeks after first stroke using BDI among 100 patients aged 27-70 (Berg et al. 2001).
31-40%: 32% with minor depression inclusive at two months (Morris et al. 1990);
an estimate of 33% at 100 days post-stroke using CESD (Weimar et al. 2002); 34% as
assessed by DSM IV and the BDI at six months after stroke (Toso et al. 2004); 38%
showing a one-year cumulative incidence including minor depression using DSM IV
(Aben et al. 2003).
The incidence at one month after stroke identified by the current study is
comparable to some previous work (Beekman et al. 1998; Berg et al. 2001). However,
the incidence can still be considered as lower than reports with incidence/cumulative
incidence ranging between 32% and 40% (Aben et al. 2003; Liu et al. 2003; Morris et
al. 1990; Toso et al. 2004; Weimar et al. 2002). The American Heart Association states
that some 11-68% of stroke survivors suffer depression and about a third have major
depression (Kelly-Hayes et al. 1998). There are different Caucasian national samples
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with a population-based study design (Burvill et al. 1995, Beekman et al. 1998,
Weimar et al. 2002, Eriksson et al. 2004, Aybek et al. 2005) with a sample ranging
from 294 in Perth (Burvill et al. 1995) to 4264 stroke survivors in Germany (Weimar et
al. 2002). If mild depressive cases are counted, the incidence is also comparable to a
prevalence study reporting 62.2% on poststroke depression among the Chinese elderly
in a rural community, using 15-item GDS with a cut-off point of 5 (Fuh et al. 1997).
In contrast to previous comparisons, the incidence of DAS identified in this study
may also be considered high with reference to a study on American patients. The
estimate lies around 10-27% (NIMH 2002) or 16.4% among 189 first-ever ischemic
stroke patients in Hong Kong using DSM III, GDS and HADS (Tang et al. 2005).
Direct comparisons among the studies should be made judiciously because of the
differences in study design, sample characteristics, methods of data collection
(self-completed or by interview) or assessment tools/cut-off points used for depression
identification. More importantly, cultural diversity may also show itself in the
emotional responses to the sudden contraction of a highly debilitating disease like
stroke. Chinese are known to be cautious in revealing their true emotions to outsiders
or at least those other than family members (Bond 1991).
4.1.3 The trend of DAS at six months and after
A big drop in the six-month incidence has also been observed in the current study,
as shown previously. Another study has remarked that the recovery pattern is
spontaneous within the first month for about half of initially depressed patients, and
suggested that the remainder are likely to develop chronic depression (Andersen 1997).
And a report that the prevalence in community samples is much lower than that in
hospital settings (Aben et al. 2001) may also reflect the local situation, when nearly all
the patients have been residing in a community at the time of the six-month interview,
whether it be their home or some other long-term care facility. Our findings show a
similarity with a follow-up study reporting a substantial decline, to 12 percent overall,
in the prevalence of depressive disorder (Morris et al. 1990). But the data were
collected fifteen months after the first stroke episode. The likely pattern beyond six
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months is therefore less certain in this study since there is a study noting two peaks in
identifying DAS (Aben et al. 2001). In contrast to the falling trend, a persistent pattern
in DAS has also been reported. Depression among a sample of 321 stroke survivors
did not decrease by the one-year follow-up (Kotila et al. 1998) or was maintained at
24% in a sample assessed by a standardised questionnaire at 15 months after stroke
(Choi-Kwon et al. 2005). Some claim that the prevalence and severity of depression
are highest between six months and two years post-stroke (Francisco 1993), or that the
incidence of DAS three years after first stroke is as high as 34%, by using a score of 14
on the BDI as a cut-off (Angeleri et al. 1997).
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4.2 Risk factors and predictors of DAS
Current study results have partly supported Lyketsos’s criteria set for
stroke-depression causality as shown below:
1.
Under the appropriate circumstances, depression and stroke occur together
the great majority of the time.
2.
There is no other coherent explanation for the depression, such as another
illness, medication, substance use, or a personal or family history of major
depression.
3.
No study has demonstrated that major depressions lead to stroke
4.
The onset of depression is within one month of the stroke.
5.
Resolution of the active phase of stroke is followed by resolution of
depression in close temporal proximity (Lyketsos et al. 1998).
Lyketsos’s criteria set for DAS have been supported in the literature except the
third proposition. Among a population-based cohort of 6,095 stroke-free white and
black men and women aged 25 to 74 years, depression is predictive of stroke across all
strata (Jonas & Mussolino 2000). Depression also predicts the risk of stroke,
specifically ischemic stroke among Japanese (Ohira et al. 2001). Another study was
able to support such findings (Krishnan 2000, Krishnan et al. 2002). Besides stroke,
men with depression within the preceding 10 years were three times more likely to
develop ischemic heart disease than the controls (Hippisley-Cox et al. 1998), or
established coronary artery disease (Krishnan et al. 2002). DAS causality prediction is
the subject of an on-going debate when cross-sectional or case-control study design is
adopted when the temporality cannot be affirmed.
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4.2.1 Bio-anatomical domain
The role of patho-physiological mechanisms in DAS remains controversial. We
can find a weak association between the stroke types (those affecting the anterior
cerebral circulation, partial or total) and DAS, indicating a possible link between
severity of brain tissue damage and DAS, in line with local and overseas findings
(Harrington & Salloway 1997, Tang et al. 2005). The finding is crucial as it will help
assessment and treatment modalities (Hinkle 1998). Even though previous studies
have hinted at the possible relationship with DAS of the lesions’ location, laterality,
brain area involvement or the size of lesions (Robinson et al. 1990, MacHale et al.
1998, Berg et al. 2001, Rao 2001, Spalletta et al. 2003, Vataja et al. 2004), we are
unable to establish adequate evidence to draw reliable conclusions. This is partly due
to data missing from scan reports or to useless information in case notes about the
stroke lesions. An attempt to prove the hypothesis of a laterality effect on DAS has
been unsuccessful. Despite Robinson's influential neuro-anatomical model and other
findings lending support to DAS being associated with left hemispheric lesion
(Robinson & Starkstein 1990, Fuh et al. 1997, Vataja et al. 2004), there are inadequate
data to confirm a laterality effect in DAS (Carson et al. 2000a, Gainotti & Marra 2002).
Studies also suggest that subcortical stroke is significantly associated with DAS
(Robinson 1990, Robinson et al. 1990, Provinciali & Coccia 2002), but that has not
been established in our study. In fact, Aybek (2005) has refuted any association
between acute emotional behaviour after stroke and neurological impairment or lesion
localisation.
To test the hypothesis proposed by Mast (2004), we have also examined whether
there is any relationship between cerebrovascular risks factors (CVRF) and DAS. The
effect of CVRF (hypertension/diabetes/cardiovascular diseases) on DAS is not
established in this study. We may need further data over a longer period for follow-up
(2+years) to see the effect of CVRF on DAS, as in previous studies (Everson et al.
1998), or a population-based study among Chinese people. Studies have also
postulated that the reduced blood velocity in the carotid artery may impact on
depression by examining Carotid Doppler findings (Tiemeier et al. 2002). However,
Carotid Doppler assessment is not routine in our study. For more than 30% of
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participants, a Carotid Doppler report is not available. To explain this, the
under-utilisation of Carotid Doppler results in the UK has also been attributed to the
possibility of ageism in stroke care (Fairhead & Rothwell 2006). Even if the results
are available, the question of whether reduced cerebrovascular reactivity among
depressed stroke patients is a cause or an effect remains largely unknown, as current
results rely mainly on a one-off examination. Some researches have turned to the
examination of any significant reduction in the cerebrovascular reactivity among
depressed patients (Neu et al. 2004). The hypothesis of carbon dioxide-induced
cerebral vasomotor reactivity is proposed as a causal factor for sub-threshold
depressive disorder (Tiemeier et al. 2002). A more recent study of the effects of
haemodynamic factors in depression in late life suggests that the observed reduction in
cerebral blood flow velocity measured by Transcranial Doppler Ultrasound could be a
result of reduced demand in more seriously depressed cases. A separate study is
warranted to verify these hypotheses.
4.2.2 Cognitive and communicative domains
Cognitive function, as reflected by scores on the Abbreviated Mental Test (AMT),
is identified as a significant factor associated with depression, as demonstrated earlier
(Kauhanen et al. 1999). However, in contrast to having a majority of stroke patients
(61.7%) reporting a cognitive decline (Pohjasvaara et al. 1997), our participants have
produced a slightly better cognitive assessment six months after first stroke. In this
study cognitive impairment of the participants is relatively mild and infrequent, as
shown by the high mean scores in the AMT at one month and six months. Despite the
high entry level of the AMT score, its decline has played a persistent and important
role in predicting depression. This is supported by the fact that the AMT score has
been a highly significant determinant for DAS. As in previous findings, we have also
found an association between DAS and cognitive impairment (Leonard & Myint 2006)
especially in terms of memory defect (Kauhanen et al. 1999, Liu et al. 2003, Vinkers et
al. 2004). In this study, the change in depression can be predicted by a change in
memory function. Its role is more evident with a longer time frame post-stroke (six
months in this study), as demonstrated by a previous study (Kauhanen et al. 1999).
Comorbidity of dementia among the depressed group may be highly suspected, though
the temporality and causality between stroke, dementia and depression in this sample
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are difficult to prove. We produced an assessment of AMT at two interviews after
stroke but not before stroke onset. Thus we may not have adequate evidence to rule out
the pre-clinical development of any mild cognitive impairment. In fact, previous work
examining cognitive function among a cohort of 74 stroke subjects in the Framingham
Study suggested that the intellectual decline in post-stroke patients appeared to be
independent of the presence of depression (Kase et al. 1998). Other research indicates
that the presence of depression at baseline does not seem to accelerate cognitive
decline that has been monitored over a longer time frame (Vinkers et al. 2004). This is
contradictory to our study results. A deteriorating trend in the AMT score among the
depressed group has been noted at six months. In addition, the baseline AMT score is
one of the predictors accounting for the change in depression as assessed by GDS. It is
also one of the final predictors for depression at six months when DSM and GDS
assessments are used. It indicates a longer follow-up study to clarify the result.
Whether the resolution of depression can attenuate the cognitive function or the
opposite direction is the true story requires further study and a longer follow-up as
suggested before (Dam 2001, Berg et al. 2003). However Murata et al. (2000) further
proposes that the mood improvement at follow-up is found to lead to a greater
recovery in cognitive functioning. According to this observation, they suggest that
DAS has led to cognitive impairment and not vice versa. Since our study is not
delivering any intervention, we may not have evidence to support or refute such a
proposal.
The communicative domain in DAS is relatively less significant than that
reported previously (Hosking et al. 1996, Kauhanen et al. 1999). The inability to
demonstrate the persistent effect of aphasia in depression may be due to the fact that
aphasia is not common or mild among the recruited sample, as severe aphasia was one
of the exclusion criteria during subject recruitment. Those reporting communicative
problems including aphasia, dysarthria, or visual/hearing problems have returned to a
low level or no disturbance at all at six months. No factor in the communicative
domain is found in the ultimate regression model.
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4.2.3 Dependency and somatic symptoms domain
Under this domain, the dependency level of participants was measured by the
Barthel Index and Norton Score on Pressure Sore, with the somatic symptoms domain
reflected by the level of disturbance of ten somatic symptoms measured on a
four-point ordinal scale. Our study has identified the effect of physical factors (body
discomfort, different pains and self-care functions) on DAS as in line with previous
findings (Geiselmann et al. 2001, Gerstenlauer et al. 2003). Their effect on DAS
persists at six months. Studies have proposed that the loss of body functions or
increased dependency levels are the root cause of DAS at the acute stage (Nys et al.
2005), or years after discharge (Jones et al. 2000). Among the somatic symptoms, Yen
(2003) suggests that unresolved neuropathic pain may have a vital effect on DAS (Yen
& Chan 2003). In our study, the effect of various types of pain is less significant as
most resolve themselves to a lower frequency and intensity, except that of nonspecific
muscle pain, which increases from 11.2% to 19.7%. The urge to assess pain-associated
depression and manage pain control in older community patients with chronic pain has
been emphasised (Gerstenlauer et al. 2003). In this study, dizziness and joint pains are
associated with depression at one month and six months after stroke but neither are
determinants or predictors of DAS if DSM criteria are applied. Instead, disturbance
due to constipation and other problems at one month have taken over as the predictors
of the depression score at six months as assessed by GDS, or are replaced by
dependency levels if CESD is used. Such findings may imply an artifact as
GDS/CESD for depression is known to be void of assessments on somatic complaints.
It has been argued that patients with depressive symptoms tend to somatise during
medical consultations (Woo et al. 1994, National Institute of Mental Health 2003),
though this is not a prominent observation in this research. The tricky point is that
somatic symptoms and elevated dependency may be the cause of DAS as
demonstrated in this study, as well as the effect of DAS as treating DAS has been
shown to improve self-care outcomes (Chemerinski et al. 2001). The important point
to make is that clinicians should not overlook unresolved somatic symptoms among
stroke survivors. As for the suggestion of sub-threshold depression being a
co-morbidity with somatic illnesses and physical disability (Geiselmann et al. 2001),
we may not have adequate data to support such a conclusion.
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4.2.4 Emotional domain
Some recent studies have proposed mood factors as core determinants of DAS
(Jonkman et al. 1998, Kim et al. 1999, Kauhanen et al. 2000, Perry & McLaren 2004).
In this study, four different items in the emotional domain are found to relate to DAS:
(1) one-item questions on identifying any emotions during hospitalisation at the
one-month interview, (2) level of disturbance due to worries or various life stressors,
(3) perceived health status and disturbance due to anxiety, (4) frequency of displaying
three facial expressions (happy/flat/sad or tearing) over the past week. Our participants
have reported more worries and depression during hospital admission due to acute
stroke than other emotional symptoms such as anxiety, panic and agitation. In fact, a
previous study has suggested that depression is more common than anxiety disorder,
fatigue or psychosis among stroke survivors (Bourgeois et al. 2004).
The presence of worries/life stressors has been identified as a persistent and
important predictor of DAS. It is the most frequently reported emotion during hospital
stays. Among all worries, that relating to stroke rehabilitation is the most common, as
more than 80% of depressed participants, and 50% even among the non-depressed
group report so. Even the frequency of reporting worries drops by more than a half at
six months compared with the one-month data, and it remains one of the significant
factors associated with DAS. Studies have reported that life events such as current loss
or bereavement (Forsell 2000, Hwang et al. 2000) or other unhappy episodes in life
such as marital problems or unemployment (Kurlowicz 1997, Fung 2003) are
associated with geriatric depression. This is also reflected in the present study. The
‘level of disturbance due to losing job’ identified by GDS and the total number of life
stressors identified by CESD are determinants of DAS at six months. These factors do
not show its significant effect at one month. Whether these worries are real or merely
apparent is worth careful examination as such findings may have implications for
intervention. A comprehensive assessment and interventions of worries and life
stressors is indicated as studies have accumulated more evidence to prove the
profound damage of chronic stress in the hypothalamic-pituitary-adrenal axis and the
immune system, which in turns, acts as a trigger for depression (Leonard 2006).
At one month, the majority of the subjects, disregarding depression levels, have
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reported the worry about stroke condition that is also a predictor of DAS. The level of
worry over current health status has been identified as a determinant of DAS at one
month by DSM and CESD, as well as a predictor of the depression score at six months.
These reported worries seem justifiable after the first attack of stroke and are
supported by a current study reporting that ‘worrying about disease’ and ’worrying
about family’ can predict DAS (Li et al. 2003). The significance of such finding
affirms patients’ ability to report DAS in their language usage such as ‘worry about
current health status’ that is valid, reliable, assessable and modifiable by the joint effort
from patients, families and clinicians. The validity of this finding is also in line with
data of health literacy on depression from the same study sample who frequently
mention ‘worries’ as one of the descriptors for depression. This preliminary result may
prompt more vigorous studies to validate and replicate if patients’ worries and life
stressors may help to predict DAS in the future. And more indepth assessment tool
such as the stress inventory scale may be recommended.
In comparison with depression, the role of anxiety in this study is less obvious.
‘Level of disturbance due to anxiety’ has been shown to be one of the significant
correlates of DAS, as an earlier study also found (Beekman et al. 2000). The frequency
of reporting anxiety drops from 23% to 6% at six months. And it is not a predictor of
DAS. This is possibly due to the fact that a single-item question may not be able to
capture its presence fully, as compared with the use of a validated scale such as the
Hospital Anxiety and Depression scale (HAD) (Kim et al.). But the performance of
HAD in DAS assessment is not considered satisfactory by one local study (Tang et al.
2004).
The frequency of a sad/tearing facial expression over the week is a consistent
determinant or predictor of DAS across three assessment tools, as reported previously
(data published already, see Publications). This diagrammatic tool may have helped
older adults to reflect their emotional status more readily and directly without the need
for much verbal elaboration.
In non-English-speaking countries, diagnostic and rating tools generally accepted
in the West may be particularly difficult or impractical to apply, as low literacy levels
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are common, although variable, among local elderly patients. Moreover, traditional
diagnostic depression scales are: (1) established mainly for research purposes or drug
trials, (2) too academic to use and therefore require training, (3) not user-friendly for
under-educated communities and (4) time-consuming during first screening. The
reliability of these screening tools rests heavily on the language ability and education
level of the patient. Recent effort has been diverted to a simplified and user-friendly
tool to aid early and prompt assessment by non-specialists in healthcare to trace
high-risk patients without further aggravating the healthcare burden. Study has
attempted one question on depression assessment (Watkins et al. 2001). The variable
on self-reported depression (yes/no) was the primary outcome variable in a Swedish
national study (Eriksson et al. 2004). In our study, we have tried the use of one
question as well. We probe the existence of notable mood conditions (depression,
anxiety, worry, panic, others) during hospitalisation. It is this one question that is
identified as a very significant factor in DAS at one month as assessed by three tools.
This also justifies early assessment of depression by posing a simple question to
patients during their acute admission, as has been demonstrated before (Watkins et al.
2001). A single-item tool, asking ‘Do you often feel sad or depressed?’ has been used.
But the biggest problem in applying this strategy to Chinese people is that it is deemed
unsuccessful because of low literacy as regards ‘depression’. Additionally, when this
one-question assessment compares the responses with those obtained using a clinical
classification, the Montgomery Asberg depression rating scale (MADRS), result
shows a low positive predictive value but a high negative predictive value (Watkins et
al. 2001). Others have attempted the use of two questions (Arroll 2003), or three
questions claiming that the ‘help’ question has boosted the specificity of DAS
assessment to a satisfactory level (Arroll 2005). Therefore, the use of any simplified
tools needs further validation because of cultural specificity and to address the issue of
varied levels of depression literacy as reflected in our study.
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4.2.5 Family and social support domains
The buffering effect of family and social support has attracted an increasing
research effort (Angeleri et al. 1997, Swartzman et al. 1998). In addition, individual
life situations and coping abilities can be influential on DAS (Forsberg-Warleby et al.
2004). The added benefits of larger social networks as regards physical function and a
lower risk of institutionalisation have been suggested (Cohen-Mansfield & Taylor
1998). Thus personal resources before stroke and the social support network at six
months have been also examined. The evaluation of the use of eight means to relieve
unhappiness before stroke at the one-month interview showed that the majority of
participants did not find the above or any other means helpful nor did the use of these
means relate to depression. Only about 40% of participants reported the use of the
following means to relieve unhappiness before having the stroke: ‘watching
TV/listening to radio/music’ (43.9%), ‘going for a walk’ (38.7%), ‘finding someone to
chat to’ (32.5%) or ‘sleeping’ (23.2%). The perceived help of all these means is
considered weak. Such findings suggest that many older people do not have any
readily available or effective alternatives to relieve emotional stress. Effort in this
regard needs to be strengthened for primary and secondary emotional health
promotion. One study has also proposed the dual role of social support and the
satisfaction of material needs such as finance in the first few weeks after stroke
(Robinson et al. 1999), and this is also supported in this study. ‘Level of disturbance
due to financial problems’ is a determinant of DAS at one month by CESD. As for the
longer time frame (six months to two years after stroke), psychosocial factors may be
more important predictors for DAS (Astrom et al. 1993, Angeleri et al. 1997).
Family support might enhance quality of life among stroke survivors (Swartzman
et al. 1998, Kim et al. 1999, Jaracz & Kozubski 2003, Tang et al. 2005). In this study,
the unequivocal finding of the aggregate score on the Lubben Social Network Scale
(LSNS) being a protective factor for DAS has been demonstrated. In fact, researchers
have postulated that the diminishing effect of physical functioning and dependency
levels of stroke survivors may have been confounded by the effect of family
functioning (Clark & Smith 1999). But the role of social support studied by different
depression assessment tools is inconsistent in this study. While in DSM only the
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‘relative subscale’ has shown a relationship with DAS, GDS has identified both the
‘relative subscale’ and the ‘confidant subscale’ as factors associated with DAS, but not
the ‘family subscale’. This is contrary to the positive finding that informational
support from family, relatives and friends jointly accounts for 22.1% of the variance in
depression (Li et al. 2003). In this way, such inconsistency has weakened the effect of
this domain in explaining DAS when compared with factors in other domains. The
notion that family support is not one of the significant factors associated DAS can also
be explained. Reports have shown that families or care-givers also experience a
tremendous burden in prolonging care-giving to stroke survivors (Angeleri et al. 1993,
Anderson et al. 1995, Angeleri et al. 1997, Martin et al. 1998, Chumbler et al. 2004).
Frequency of depression among carers of stroke patients is common (Tompkins et al.
1988, Angeleri et al. 1993, Dennis et al. 1998). Thus the problem of DAS may extend
to also the family. The targeted interventions toward DAS should take both the patient
and the family into consideration from assessment to management of stroke and
depression at the same time.
4.2.6 Demographic and other factors
Age. In this study, the mean age of the depressed group is higher than that of their
counterparts. The effect of age is not straightforward, despite previous findings that
age is one of the common confounding factors on DAS (Fuh et al. 1997, Beekman et al.
2000, Kao 2000, Berg et al. 2001, 2003, Eriksson et al. 2004). Age is only identified by
DSM as a determinant for depression at one month, not by other tools or at six-month
follow-up. Such inconsistency also exists in the literature (Gesztelyi et al. 1999, Nys et
al. 2005).
Gender. That female gender enhances the risk of DAS has predominated in the
literature (Paradiso & Robinson 1998, Miller 2000, Weimar et al. 2002, Desmond et al.
2003, Eriksson et al. 2004, Cassidy et al. 2004, Tang et al. 2005). Being female as a
stroke survivor is an independent predictor of self-reported depression (Eriksson et al.
2004), and one study has noted a two-fold higher rate of both subjectively reported and
objectively rated depressive symptoms (Cassidy et al. 2004). This finding is also
supported in the current study. If the discussion is confined to Chinese people, one
study has reported that males in the old-old group (=/> 80 years old) were more prone
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to geriatric depression (Woo et al. 1994). Other studies may not identify a gender
effect on DAS (Burvill et al. 1995, Burvill et al. 1997, Buzzelli et al. 1997, Nys et al.
2005).
Marital status. In this study, ‘not living with family’ has been identified as a factor
associated with DAS at one month. This finding supports Hayee’ s (2001) suggestion
that being older than 70 and living alone after a stroke might produce a risk of DAS.
But these factors have no persistent effect in predicting depression at six months.
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4.3 DAS interventions
4.3.1 Consequence of inattention to DAS
DAS is common among older Chinese stroke survivors in the short and long term.
This is found in the current study (data published; see Publications) and has been
documented previously (Tang et al. 2005). The high incidence, one in four with DAS
at one month and one in ten at six months according to the current study, shows the
need for more attention to be given to public health interventions. The size of the
problem is an essential consideration, taking into account the fact that a quarter of the
global population is Chinese. Additionally, the high risk of cardiovascular risk factors
(hypertension, diabetes, smoking, cardiovascular diseases) in the territory (Leung
2002) may endorse the need to address the issue of DAS.
Before proceeding to discuss interventions in cases of DAS, we examine if
inattention to DAS may impact on patients’ clinical outcomes, in terms of their quality
of life and mortality, two commonly quoted indices for clinical outcomes reflected in
the this and current studies. The discussion helps to justify the need to intervene in
cases of DAS, but has not simply relied on previous papers where the findings on
clinical outcomes for patients with DAS can be misleading or inapplicable to local
settings.
Quality of life. In the current study, the quality of life of the majority of stroke
survivors did not improve at six months, which supports earlier findings on a
compromised quality of life (QoL) among stroke survivors in short and longer time
spans
(Sturm et al. 2002, Moon et al. 2004). This study also supports findings that a
further drop in QoL after the acute stage post-stroke is possible among depressed
participants (Suenkeler et al. 2002). Emotional domain factors have demonstrated an
indirect though indisputable role in post-stroke quality of life. This study has found
that the level of worry over perceived health status in the early stages after stroke
does help to predict quality of life at six months. But our study does not have
adequate evidence to support the notion that either anxiety or depression at one
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month may predict quality of life at six months. Despite this, the role of depression
and social-support factors at six months as determinants of PSQoL at six months is in
line with other studies (Jonkman et al. 1998, Kim et al. 1999, Kauhanen et al. 2000,
Perry & McLaren 2004). In addition, patients’ levels of worry about their perceived
health status at an early stage after stroke have been identified as a predictor of
depression (data published already, see Publications). An assessment of mood
including worries should therefore be administered early to salvage a compromised
quality of life in the long run (Sturm et al. 2002, Moon et al. 2004). Clinicians
working on the clinical front-line may need to monitor factors falling into the
cognitive, emotional and physical domains, factors which are modifiable for positive
changes. In particular, interventions to foster self-care functions, to alleviate worries
that may be either valid or merely apparent and due to irrational thoughts, or to target
early mild cognitive impairment might be indicated. Efforts in that respect have been
supported in that perceptions of control six months after discharge have been found
to add significantly to the predictive equations on quality of life (Johnston et al.
2004).
Mortality.
The identification of a relationship between mortality and DAS is
worthwhile as mortality is taken as a common healthcare outcome indicator. DAS,
together with dependency and somatic symptoms, has been found to be associated
with a higher mortality (Herrmann 1999). It has been further suggested that the
inability to lead an independent life at three months post stroke could be the only
predictor of mortality in the first year (Sonde & Viitanen 2001, Pohjasvaara et al.
2002). Previous studies have reported a relationship between mortality and DAS
(Robinson 1997, Everson et al. 1998, Gupta et al. 2002, Williams et al. 2004) even
after adjustment for factors associated with stroke severity at 12 or 24 months (House
et al. 2001). In this study, mortality in the depressed group is higher than in the
non-depressed group, though a statistically significant difference has not been attested.
Previously the development of suicidal tendencies among stroke survivors has been
noted (Kishi et al. 1996, Kishi et al. 2001, Pohjasvaara et al. 2001). The examination of
suicidality is addressed indirectly in this study by retrieving such documentation in
patients’ medical record. Quality of data is highly subject to a list of factors: (1)
Clinicians’ preference, knowledge or skill to assess mood or suicidality of patients
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admitted for stroke (Peveler et al. 2002). (2) It is not a norm for healthcare
professionals to document data related to emotions. (3) Some may wish to do so but
for a busy work schedule and competing clinical demand, thus only little or partial
information on emotions has been entered. (4) Researcher may miss some documented
information in view of the lengthy record or availability of the record which might
have been sent out /kept by hospital staff. (5) Such findings may not be surprising as
reflected by the study participants’ limited literacy level on depression as well as the
low detection rate of DAS acknowledged before (Gupta et al. 2002) and in this study
(data already published; see Publications). Therefore it is likely that the database on
emotional documentation is not exhausted.
4.3.2 Primary preventive measures
The rationale for primary preventive measures against DAS is the triple burden
the condition brings, as elaborated in the literature review sections and supported by
the findings on clinical outcome indicators set out above. The core concerns are early
recognition and means for detection of DAS, based on current findings. An
understanding of the hidden issues also forms one of the first steps to primary
preventive measures. We need to acknowledge the nature and intensity of existing
problems in the recognition and detection of depression among older stroke patients.
The answer to these questions is of prime importance as the information will help
guide any rational interventions to address existing problems where they exist.
Regarding intervention of DAS, the design of the study is not qualified to
conclude any valuable points, hence further experimental study in clinical settings
using (randomized controlled trial) and community in terms of health educational
procedures will reveal information to intervene DAS.
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4.3.2.1 Recognition of DAS - depression literacy
While health literacy as regards depression directly influences early recognition
of depression, and improvement in depression literacy fosters changes in attitude,
health-seeking behaviour (Lauber et al. 2003) and health outcomes (Baker 2006), little
discussion of the construct ‘depression’ among Cantonese Chinese was found in the
literature. Studies caution against applying the translated Western term ‘depression’ in
the local context, as the term can be interpreted differently among different ethnic
groups (Patel et al. 2001). The answers to the questions ‘What is depression?’ and
‘How will depression be interpreted?’ among older Chinese adults remain largely
unknown.
In our study, the majority of the interviewees (60%) were unable to describe
‘depression’. Some said they had never heard of it. Under the affective domains, some
subjects were unable to describe depression specifically, as shown by the use of vague
terms such as ‘in a bad mood’/ ‘a heavy heart’ /‘feeling uncomfortable’. Most of these
descriptors are not found in the fourth edition of the Diagnostic and Statistical Manual
(DSM IV) (American Psychiatric Association 1994) or in the Geriatric Depression
Scale short form, Chinese version (Brink et al. 1982, Lee 1994)/Center of
Epidemiologic Studies for Depression, Chinese version (Radloff 1977, Boey 1999). It
shows that the understanding or description of depression given by most older
participants is brief, vague, superficial, unspecific and far from meeting the diagnostic
criteria set in the gold standard (DSM IV) or by common assessment tools for
depression. The use of descriptors/words by most interviewees is very limited – most
illustrations falling into one of the three domains (affective, behavioural or cognitive)
only. Some have incorporated terms from two domains. But the worry is that the most
frequently cited descriptors, such as ‘unhappy’, ‘bored/in a fidget’, ‘irritation’, are
unlikely to be indicative of depression as a medical disease when they are presented to
clinicians.
The difficulty of detecting depression may be compounded by the patients
themselves. Worse still, a very popular and commonly accepted proverb reflects much
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of the Chinese belief patterns among the elderly population: ‘Unsatisfactory outcomes
are common, occurring nine out of ten times’. This common belief shared by Chinese
patients might mask a depressed mood and fail to tip the health worker to the need for
further medical attention. Chinese people also tend to be rather reserved in their
displays of emotion. They consider displaying sadness or unhappiness in front of
people outside the family circle inappropriate because of the ‘loss of face’ issue (Bond
1991). This is supported by the numerous responses that show avoidance of or
passiveness towards depression. One study, however, presents a different finding on
the issue. The patients studied cited a lack of people to confide in as contributing to
their emotional problems (Alexander 2001). This may also apply in this study sample
as only a quarter of patients can/will choose to chat with someone when they are
unhappy before the stroke onset. Indeed, a recent study has found two streams of lay
opinion regarding depression. One group treats depression as a life crisis, implying
that depression might be considered a ‘normal life event’, while the other prefers a
problem-solving approach to tackle any occurrence of depressive symptoms as a
medical problem (Lauber et al. 2001). A poor literacy level in respect of depression
may thus further perpetuate belief in mood episodes and favour the acceptance of
persistent mood disturbance among the sufferers. And the situation may be more
common among older people who are passive in health decision-making. This study
also supports previous findings regarding reluctance among older adults to seek help
for their emotional needs (Fisher & Goldney 2003). Two thirds of patients reported
they were not interested in knowing about depression as a medical disease. In this way,
only four subjects remarked that the mood condition - depression - was or might turn
into a disease. This is an important finding as patients’ inclinations and views on
mental health will affect their health decision-making and subsequent health outcomes.
Studies have demonstrated that those who regard depression as a medical illness are
more receptive to psycho-pharmacology, while others perceiving the condition as a
psychological matter would turn to psychotherapy as a natural remedy (Lauber et al.
2003).
Following from this discussion, we are justified in suggesting that mental health
literacy among older adults must be addressed as an important public health issue if
depression and old age are recognised as growing concerns in healthcare. To raise
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depression literacy, the joint efforts of healthcare authorities, clinicians, older adults
and their families are indicated to achieve effectiveness. This is supported by a finding
that medical societies/associations and health politics in some European countries are
core factors that contribute to an adequate service tackling depression in later life
(Bramesfeld 2003).
4.3.2.2 Early detection of DAS – use of smiley diagrams
Problem in early detection. This study has supported the notion that patients with
DAS have largely remained unattended (Gupta et al. 2002). The detection or
assessment of DAS is therefore seen as a crucial step, in particular at the acute phase
when mild disease tends to have better remission than when it has become a
long-standing disease progression. Result in our study also shows some treatment
efficacy of early intervention of the depressed stroke subjects. All those under the care
of psychiatrists are no longer depressed at six months when detection and treatment of
DAS has been prompted early. But there are many problems for such undertaking.
The reasons for healthcare workers to set DAS aside are multi-faceted. The
inability among patients to recognise depression has been explored under the previous
section on health literacy in respect of depression among stroke survivors. Two other
perspectives hindering early detection of DAS among Chinese people are discussed:
(1) clinicians’ concerns and (2) the lack of unified assessment tools.
Some of these clinical difficulties encountered by healthcare professionals as the
main hurdle in the assessment of depression have been documented (Chew-Graham et
al. 2004). In a previous local report, healthcare professionals admitted that time
constraints and competing demands in busy geriatric wards formed one of the most
prominent hindrances to conducting a mood assessment on the client (Lee 2002).
Some admit that they may not keep track of the new and rapid development of current
and efficacious anti-depressants or alternative therapies available for depressed stroke
patients (Farrell 2004), and are thus less motivated to carry out screening. Other
healthcare professionals believe that mood and mental health need to be handled by
clinicians with a psychiatric background. Other reasons are related to knowledge and
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skill deficit or ageism (Boey & Chi 1999, McCurren et al. 1999, Peveler et al. 2002) .
A low index of suspicion of depression and the low priority given to handling mood
problems in acute settings were the reasons proposed by geriatricians in local settings
(Lee 2002). Against such a background, verbal cues for early detection and diagnosis
are likely to be missed. Interestingly, some geriatricians have admitted to withholding
the use of the term ‘depression’ in Cantonese from some older patients who might not
understand or accept having depression at all (Lee 2002). This is partly supported by
the previous discussion on literacy levels in respect of depression.
The lack of a biological marker as an assessment tool in the case of depression
has been the major barrier to prompt clinical diagnosis. The selection of an appropriate
tool to diagnose depression in the developing world, with a wide range of literacy, has
been a challenge. With respect to the instruments, the reliability and validity of
imported types for the local population remains a concern, because of cultural
specificity (data published already, see Publications). To strike a balance between
better validity and patients’ tolerance of a comprehensive tool is not easy and remains
an ongoing task. Thus researchers have been keen to identify a simplified version of
available tools to resolve the problem (Anderson 1996, Watkins et al. 2001, Arroll
2003). At the same time, any simplified screening is also attacked in that categorical
representations of important clinical phenomena can misrepresent dimensional
quantities (Nease 2003).
An alternative to prompt early detection. Working along these lines, this study has
attempted the use of a diagrammatic tool to prompt depression assessment among
Chinese, who are more reserved in their emotional disclosure and limit such
expression of mood mainly to family members (Tung 1991). More importantly, only
40% of the participants are able to interpret ‘depression’ in their own words and the
rest report not knowing or having heard such a term at all. Earlier discussion has put up
a conceptual background to this pilot study on the utility of a diagrammatic tool for
DAS assessment on top of a questionnaire, a common assessment tool. The universally
understood smiley diagram might be a feasible alternative to assess depression even
for those with a low literacy level, a language barrier or mild expressive aphasia. The
study has presented preliminary findings to support such a proposition. The
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justification is that the emoticon (sad) shows good correlation with DSM IV as indicated
by its Kappa value and other screening properties including specificity, and positive
and negative predictive values and is also comparable with that of GDS/CESD, which
takes about 5-10 minutes for completion.
Utility of smiley diagrams in DAS detection.
The use of smiley diagrams in
prompting DAS detection has been discussed and published recently (see
Publications). When stroke victims report a high frequency of the emoticon (flat) and a
low frequency of the emoticon (happy), the clinician might still need to maintain a high
index of suspicion of depression rather than exempting patients from further
assessment. The unexpected findings in the case of the emoticon
(happy)
have alerted
us to the use of screening instruments when cultural specificity is uncertain. Previous
literature has remarked the strong cultural differences in the frequency, duration and
intensity with which various emotions are felt among Chinese and other cultures
(Bond 1991), in that Chinese tend to moderate their expressive emotions compared
with Westerners (Bond 1991).
In this study, GDS/CESD has also been used concurrently for depression
assessment. While GDS/CESD is a clinically acceptable and widely used tool to detect
DAS, the three emoticons, which take a shorter time for completion, being less wordy
and more acceptable to illiterate patient groups, might also help to detect and monitor
early depression when depression assessment is not routine in medical settings for
stroke patients because of time constraints (Lee 2002).
However, the routine depression assessment has been challenged. A recent
systematic review has shown that detection of depression using different assessment
tools may not necessarily guarantee provision of treatment by clinicians (Schreiner et
al. 2001). A formal channel of communication may be necessary to alert clinicians to
warning signs rather than relying on the current mechanism of routine screening
(Gilbody et al. 2005). In the same sample, fewer than one in ten stroke patients gained
access to mental healthcare facilities at 6 months after stroke, despite nearly a quarter
of a large sample being identified as depressed at one month (data published already,
see Publications). It is therefore likely that only patients with marked DAS are
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captured by current systems.
4.3.3 Treatment modalities of DAS
Previous discussion has focused on recognition and detections of DAS in local
Chinese people as the initial step before intervention. This section follows on to ask
how to improve the quality of stroke care, as it is still under scrutiny (Mayor 2007).
Once DAS is confirmed, effective treatment modalities are indicated. This section will
discuss the available evidence on two types of treatment of DAS as identified in the
literature: (1) pharmacological treatment, mainly anti-depressants and (2)
non-pharmacological interventions.
4.3.3.1 Pharmacological treatment
The effect of anti-depressants on DAS has also been demonstrated in this study,
though statistical significance cannot be established with so small a sample. All
patients under the care of formal mental health service are found non-depressed.
Among them, ten have been taking anti-depressant. This may give some preliminary
evidence to show the efficacy of anti-depressant and the added benefit of medical
surveillance. The argument is that if this group of milder stroke patients can benefit
from anti-depressant treatment, the effect for the moderate to severe grade of stroke
victims might be even more prominent or justified though they are commonly
exempted from receiving active psychoactive treatment due to concomitant problems
in speech and cognitions. Previous studies on anti-depressants have produced
interesting findings. Both fluoxetine and sertraline are found to be efficacious
treatments for DAS; it is also suggested that left stroke can be a predictor of treatment
resistance (Spalletta et al. 2003). One study has tested the preventive effect of giving
Mirtazapine on the first day post-stroke and claims that it may prevent 34 cases of
depression per 100 stroke patients, leading to a significant reduction in depression
incidence (Goldberg 2005). As for the efficacy of the drugs in question, 67.5% patients
using anti-depressant drugs do not report depressive mood (Eriksson et al. 2004). The
findings suggest that reducing premorbid levels of depressive symptoms (initial stroke
admission) or increasing positive affect may help the recovery process (Ostir et al.
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2002).
The benefits of using anti-depressants have been documented, where efficacy is
regarded as a change in the Hamilton Depression Rating Scales (usually a reduction of
50% or a drop below a pre-specified score). A systematic analysis of nine selected
randomised control trials between 1986 and 2001 affirmed the efficacy of using oral
anti-depressants (including classical and related tricyclic anti-depressants or SSRI) to
curb DAS (Mottram 2006). In addition, SSRIs are reported to have fewer side effects
than classical tricyclic anti-depressants in terms of withdrawal rate (Bhogal et al.
2005). Studies have established that treating DAS with anti-depressants has
significantly impacted on positive self-care outcomes (Battaglia & Bejor 2001;
Chemerinski et al. 2001), and thereby improves the quality of life among stroke
survivors. If DAS is left unattended, a deleterious influence on functional recovery is
likely (Gainotti & Marra 2002). Recent study shows that anti-depressant therapy
produces a significant improvement in various aspects of cognitive performance,
including attention and abstract reasoning (Battaglia & Bejor 2001). There is another
inflammatory hypothesis to explain the possible anti-depressant effect. It is found that
effective anti-depressant treatment of depressed patients also reduces the
inflammatory changes (Leonard & Song 1999, Leonard 2006). Though exact
mechanism is not fully understood, studies have suggested the anti-inflammatory
actions of anti-depressant may indirectly modulate central monamine dysfunctions in
depression
by
improving
the
immune
and
endocrine
system
producing
neuro-protection (Leonard 2006). In fact, studies have argued that pharmacological
treatment must be used with moderate and severely depressed stroke patients to trigger
their cognitive or psychomotor engine, at a functional level, before psychotherapy can
be effective (Battaglia & Bejor 2001).
4.3.3.2 Non-pharmacological treatment
Physical/social activity. Depending on the proposed etiology of DAS, previous
efforts have targeted improving physical and cognitive functions, social activity or the
prevention and management of post-stroke complications (McGuire & Harvey 1999,
Lai et al. 2004, Moroz et al. 2004, Merino et al. 2005). A recent study has claimed that
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physical activity protects against depression (Strawbridge et al. 2002). A comparable
intervention with stroke survivors is extended physiotherapy. Physical training seems
to facilitate symptom relief in stroke rehabilitation (Harari et al. 2004).
Cognitive therapy.
Some DAS interventions target the worries perpetuated by
irrational thoughts. Cognitive therapy, providing information and monitoring
psychological factors (Clark & Smith 1999), has been used to alleviate depressive
symptoms among stroke survivors. Some cognitive theorists attribute DAS to
irrationality and erroneous cognition in the thinking process. The rationale of
cognitive therapy claims to reduce negative attitudes among stroke survivors (Lewis et
al. 2001) and moderate the perceptions of control. This argument has lent some
support by a study showing that depressed stroke patients have significantly less
positive cognition than the non-depressed (Nicholl et al. 2002). This cannot be verified
in our study even though some subjects have attempted to interpret depression as
having negative thoughts, using descriptors like ‘unhappy things’, ‘gloomy world’,
‘painful and bad things’, ‘no-good things’, or ‘thinking at one side/about the
negatives’. But the majority of the sample (60%) cannot explain what ‘depression’ is.
In that case, we are not sure if erroneous thoughts are the culprits inducing depression,
in particular among the depressed group, though the ability to define depression is not
found to be related to depression levels in this sample.
Family support. The literature has remarked the role and style of problem-solving
among Chinese families. The family is significant in providing resources, fulfilment,
protection and welfare to its members (Young 1995), and turning to outside sources for
support is considered to be the last resort (Chan 1995), because of the face issue (Bond
1991, Tung 1991). But in this study, we have pointed out the relatively weak utility of
family networks in fostering patients’ emotional health. Thus, there is a need to
facilitate and expand family support or to extend any available financial or healthcare
resources following the acute phase (Robinson et al. 1999). Innovations such as
interventions directed towards the family may be indicated. More evidence-based
study on current strategies such as the use of integrated pathway and systematic
discharge support is therefore indicated.
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4.4 Limitations of study
4.4.1 Threats to internal validity
4.4.1.1 Selection bias
Selection bias is one of the most problematic and frequently encountered threats
to the internal validity of studies not using an experimental design (Polit & Beck 2004).
The application of inclusion and exclusion criteria to subject recruitment has reduced
the heterogeneity of the studied sample. But at the same time the very ill, the severely
aphasic, those cognitively impaired and the seriously depressed are likely to be
excluded. Eligible subjects will be relatively milder in their stroke condition.
Underestimation of DAS is possible, which might explain the lower incidence of DAS
in the Hong Kong sample compared with Caucasian samples.
The 50% recruitment rate by a part-time research nurse should be considered
satisfactory. Three tiers of problems might lead to major practical and logistic
difficulties encountered in subject recruitment process:
Patient-oriented. In hindsight, potential candidates may have refused to join the
study for the following reasons: (1) Patients are uncertain about the importance of
DAS due to a low level of literacy regarding depression, in particular the female
subjects who were found less likely to be recruited to the study. In fact, the decision to
include or exclude subjects was also validated by the staff nurses or the stroke
coordinator in the stroke unit. (2) Some mention an inability to read and write, or
unfamiliarity with what is research in particular the group with low/no formal
education, and so cannot complete the required consent form. (3) Some patients are
not motivated to learn or bother about depression, but only about stroke, as the latter is
the diagnosis for current admission. (4) Some dislike having their mood state
examined and disclosed. (5) A few may fear committing themselves to the study due to
personal plans. (6) Some patients are unwilling to be the subject of research,
irrespective of the reasons. A few (n=8) in fact withdrew even after giving their
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consent to join the study. Most of those withdrawing early were deterred by family
members, including one medical doctor who refused to let his mother to participate in
the study despite patient’s willingness to do so. The reason given was that the family
instead of the patient’s wish should be respected. Some withdrew because of time
constraints or simple lack of interest.
Nurse-oriented. Some potential participants might have been missed due to several
reasons: (1) the research nurse was the only one responsible for recruiting subject
since the possibility of collaboration with ward staff or recruiting other suitable
research nurse was not successful; (2) being a full-time teacher during the study period,
the research nurse could only afford two to three visits every week to the target
hospital which located at the further end from work place and took at least 20 minutes’
drive if there was no traffic jam; (3) the nurse researcher has to tackle multiple and
competing tasks during hospital visits, such as obtaining patients’ consent, doing
interviews, tracing the patients’ medical record, clarification of disease nature with
stroke nurse, photocopy of relevant laboratory result, ensuring patients’ follow-up date
and time, confirming follow-up details such as venue…(4) the nurse had to meet a lot
of logistic and clinical uncertainties such as clashing with ward routine or patients’
investigation or physiotherapy sessions, lack of interview room for interviews or
patients’ defaulting the follow-up due to personal or medical conditions…(5) the nurse
had to dash for several sites to meet the patients such as stroke unit, geriatric day
hospital, specialist outpatient clinic, general outpatient clinic, waiting rooms for
speech, occupational or physical therapies.
Hospital policy. The rest either refused or were missed because of early discharge or
a change in hospital admission policy whereby not all stroke cases were admitted to
the stroke unit - the agreed study site. Among all ischemic stroke patients, nearly half
the subjects were excluded after application of exclusion criteria. Despite such efforts,
about a quarter of eligible subjects could not be reached, which might be explained by
the short length of stay in acute settings (3-7 days, particularly milder cases). Potential
subjects tended to refrain from participation by phone contact, which was not
recommended in the approved protocol - no personal data should be accessed without
patients’ consent to the study.
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4.4.1.2 Study design
Inadequacy of study design poses a threat to the internal and external validity of
results, thus limiting the generalisability and replication of the findings. This study
attempts to identify the incidence of DAS among Hong Kong Chinese subjects with
first ischemic stroke. A longitudinal study design was undertaken and subjects were
interviewed at one month and six months after stroke onset. The non-DAS clients were
naturally taken as the comparison group. Two major problems with the study design
had to be addressed: its longitudinal nature and the lack of a control group.
To obtain data on the incidence of disease from an inception cohort, either a
population-based study with a random sample or a prospective cohort study design are
preferable to a longitudinal design. The longitudinal study is a commonly adopted
design in the case of stroke and depression (Bracke 1998, Hwang et al. 2000, Whyte et
al. 2004, de Coster et al. 2005, Thomas & Lincoln 2006) to help describe and evaluate
disease progress and the effect of time changes on the natural course of a health
problem or recovery (Stommel 2004b). But the lack of a control group to make
comparisons between the exposed and the non-exposed groups (in this case, exposure
refers to stroke) may render the result problematic when a background risk of geriatric
depression cannot be ruled out. But in clinical study, the researcher encounters
limitations in adding a control group, for several major reasons:
Resources: nearly 50% of eligible subjects were missed during recruitment. This
can be explained by the lack of full-time research staff in the stroke unit and by
inadequate funding. The research team had devised alternative ways of recruiting.
Junior researchers were employed for subject recruitment but the recruitment rate was
low because of difficulty in engaging older sick people, those mildly aphasic or with a
language barrier. A high wastage of recruited subjects occurred because access to
medical records was inadequate, except for the principal researcher. Another
unsuccessful attempt was made to seek collaboration from nursing staff in the stroke
unit, including the stroke nurse coordinator, but this failed because of a busy ward
schedule and the staff’s lack of interest. The inclusion of two regional hospitals in the
pilot phase was another strategy to speed up the recruitment process. All these showed
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evidence of the researcher’s effort to improve the study. But when the recruitment of
the main subjects is problematic a control group is a definite burden on resources.
A suitable control group: capturing a group of older people who are free from
cardiovascular risks, including hypertension, diabetes or ischemic heart disease, is not
a straightforward task, as evidence has shown that subjects bearing cardiovascular
risks are highly prone to depression and likely to introduce a confounding factor (Mast
2002). The idea of having a control was reluctantly rejected because of the
unavailability of additional resources and a pessimistic control recruitment-rate when
neither stroke nor depression are the immediate concerns of potential participants.
To improve a study, either a probabilistic population study or a cohort design is
preferable when resources are less of a problem.
4.4.1.3 History
History refers to the occurrence of external events that take place concurrently
with the independent variable that can affect the dependent variables (Polit & Beck
2004). Although this is not an intervention study, the spreading of the depression
message during a study period that lasted a full year might well have spread and
fostered an awareness of the disease. The counter-argument is that this will apply to
the whole cohort irrespective of depression levels. Additionally, a significant
inter-group difference could not be identified in the case of depression literacy.
Like most mood problems, depression can be induced by life stressors that
coincide with or surface during stroke episodes. Environmental factors may also play a
role in DAS (Remer-Osborn 1998). A background risk of geriatric depression may
exist. Deviation from true depression scores may possibly be caused by unpredictable
life events.
Any recent events may colour the respondents’ experience and thus affect their
behaviour and responses during the two interviews. The ‘genuine answers’ of
participants regarding mood assessment were highly moderated by media shown
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during the study period, which lasted a year. In 2004 a local celebrity attempted suicide,
probably because of depression, and the discussion on ‘depression’ in the media
became more frequent. Despite such publicity, only 40% of the participants had heard
of or were able to explain ‘depression’, and most of the descriptors they used were
rather simple and non-specific. To discover if reactive depression had coincided with
DAS, concomitant life events were inquired into at the two interviews. This
confounding factor may therefore be ruled out and adjusted for in the analysis.
The validity of depression scores may also be challenged when there is a long
interval between baseline study and follow-up. Amelioration or symptom relief may
occur because of private treatment, leading to a dilution effect on the depressive state.
This was partly avoided by having a six-month rather than a one-year follow-up. On
top of this, documented medications at both follow-ups were carefully evaluated, in
particular psychoactive agents. However, the possibility of other medications
prescribed by general practitioners could not be excluded from the current study.
4.4.1.4 Maturation
Maturation refers to processes occurring within subjects during the course of the
study as s result of the passage of time (Polit & Beck 2004). Thus changes in
depression may be due to any change that occurs as a function of time, including
emotional maturity or adaptation to disability or acceptance of the disease. In a
longitudinal design, the transition or adaptation of the disease process is well observed.
Here we note the vast drop in depression (29% to 11%) at six months after the first
stroke onset. Some may attribute such change to maturation, but this view is in
opposition to the fact that changes in depression scores occur to the same extent in
both depressed and non-depressed groups, as evidenced by the insignificant mean
difference. Also, the frequency of depression and the effect of maturation may not
have stabilised at six months after stroke. Berg’s study (2003) reported that nearly half
of those who were depressive during the first two months were also depressive at 12 or
18 months., thus may need longer duration for follow-up study. Previous study has
shown that depression may persist as long as seven years after stroke (Dam 2001)
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4.4.1.5 Mortality
Attrition due to mortality may also be acute in clinical studies. It can be argued
that those who remain in the study are unrepresentative of the group as a whole, since
the depressed group may have withdrawn, been institutionalised and been lost to
follow up. It might thus appear that depression scores in the depression group decline
over time, but the decline might only be evidence of biased attrition. In our study, the
overall dropout rate is 12.1% from one-month to six-month. The analysis of this
dropout does not support any significant difference between depressed and
non-depressed groups. During the sample estimation, we actually took the dropouts
into account and increased the sample size by 50%. The mortality was estimated to be
5.1% (11 out of 214), which is within the range reported by local research in stroke
care (20% for cortical infarct at one month and 1% for lacunar infarct at one month)
(Yu et al. 2003). It has been suggested that the attritional risk is especially great when
the follow-up period is long. Therefore a six-month follow-up was arranged. To
minimise losses at follow-up, the researcher double-checked the phone number at the
consent stage.
4.4.2 Threats to external validity
4.4.2.1 Sample representativeness
Consecutive eligible first-ever ischemic stroke patients in the stroke unit of a
regional hospital under the Hong Kong Hospital Authority, over a one-year span (1
June 2004 to 31 May 2005) were recruited. The adoption of one-year subjects helps to
avoid any seasonal effects of depression or stroke admission. Within the study sample,
81.7% suffered from ischemia stroke. This is a bit higher than the figure reported
previously, that ischemic stroke accounts for 70% of all stroke admissions (Yu et al.
2003). Participants’ mean age was 72 years, not far from that of the general population
in the older age group (65+) (Department of Statistics Hong Kong 2006). The
recruitment of more males than females (5:3) might be explained by significantly
lower education levels and more language barriers among female clients, who were
mostly housewives and might be reluctant to disclose emotions or discuss a topic they
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might not know at all with an unknown researcher. And depression literacy was found
to be associated only with the level of formal education received, and not with
depression or other socio-demographic factors.
The sample was financially more dependent, as a quarter of the participants were
living on public assistance and slightly less than two thirds regarded their
spouse/children as the main source of financial support. The need for public assistance
is higher than current government indications - that less than 20% of 700,000 older
citizens (age 65 or over) are receiving disability allowance, and less than 10% are
living in institutions (Social Welfare Department 2003). On the other hand, this cohort
seemingly has better social support in terms of the following two observations. Two
thirds were living with family members and only five participants were residents of
institutions before stroke. The medical and health background of the sample is in line
with a previous cross-sectional study capturing 1,300 local elderly community
dwellers: 13% were disease-free, about half had one to two chronic illnesses before
stroke, and the rest had three or more chronic health problems (Lee 2003).
4.4.2.2 Reliability of reporting ‘depression’ in Chinese stroke patients in Hong
Kong hospitals
The reliability and validity of ‘depression’ as interpreted by patients can be
challenged. Two thirds of participants were depression illiterate, and the
understanding of ‘depression’ is also culturally specific (Abas 1997). A study among
local geriatricians also notes their reluctance to use the term ‘depression’ as they doubt
patients’ ability to capture its full meaning as a medical disease (Lee 2002). In this
study the exact term ‘depression’ in Chinese was mentioned three times: in CESD, in
seeking what they understood by the term ‘depression’, and in asking about the type of
emotions experienced during stroke admission. This is why the assessment of
depression is explored alternatively through the use of different validated tools, as
described in Chapter 2. It is suggested that a validation study on depression literacy
and the use of a clear definition of ‘depression’ before the actual study may further
improve the validity of study results.
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Still the initiation to discuss ‘depression’ in Chinese language poses a real
challenge despite the use of a senior nurse. The data may be as futile as it is in
particular older adults who are under-educated as shown in this study or speaking a
different dialect other than Cantonese that is generally adopted by the majority local
residents. The concept of ‘depression’ has been only emerging recently (less than a
decade) in Hong Kong. Most people, including healthcare professionals, may not
readily accept the perspective of taking ‘depression as a medical disease’; rather,
depression is referred to as a psychiatric condition that is a notorious stigma. These
concepts are new to patients and its discussion is likely explorative but worthwhile to
form a ground work for further study. Another difficulty is probably related to the
non-cooperative attitude of the exhausted and sick patients during the study. Male
patients, in particular, might suspect the purpose or indications of such discussion as
reflected by a minority of the consented participants actually withdrew from study or
gave only superficial information once their stroke symptoms had been resolved.
4.4.2.3 Measurement effects
Measurement effects refer to the possible moderation of results through different
data collection procedures (Stommel 2004). The following discussion explores some
possible bias introduced by the data collection procedures.
Interview ‘flow’. Despite the use of a structured questionnaire, procedures for
assessing mood cannot be standardised. Four different instruments/tools were adopted
to assess depression. Of the four, one, DSM IV, is the gold standard for assessing
depression, two were screening instruments that have been validated locally with good
reliability and validity. But they are not flawless. In fact, Blazer (1997) has challenged
the ability of DSM criteria to capture geriatric depression. Its use can be problematic
among older people who do not display the typical symptom profiles depicted by the
criteria set. In the use of screening tools (GDS and CESD), false positives are common
as reported by previous studies, especially those undertaken by untrained staff
(Lincoln et al. 2003). GDS assessment requires binary answers that older people can
capture more readily but precision of measurement may be compromised. On the other
hand, the more answer options given in CESD have enhanced its measurement
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properties but complicated the assessment, which was not fully accepted by some
participants, in particular those who were less educated, mildly aphasic and or
speaking a different dialect. Despite these arguments, the consistency between DSM,
GDS and CESD as shown by the kappa’s value is satisfactory (> 0.4). And the
incidence rates of DAS identified by the three tools were fairly close together,
supporting their use in the current study. Some participants might feel over-used when
their mood was assessed repeatedly, with four different tools. Thus an experienced and
persistent researcher is required, to enhance the quality of the study in terms of ability
to engage and motivate the participants to expose their mood condition. Some logistic
and technical problems might also be overcome to minimise variability of data
collection in widely diversified healthcare settings.
Controlling for extraneous variables.
Technical difficulties, especially the
uncontrollable time and venues of interviews, can be an issue. The data collection sites
included specialist out-patient clinics, general out-patient clinics and patients’ homes,
if a pre-scheduled follow-up date had been missed. While some participants were
anticipating the medical follow-up at outpatient clinic for the stroke episode, they were
less inclined to an interview assessing their mood and depression level. The
competition for time can be intense sometimes, and the quality of an interview might
be subject to a few inevitable environmental interruptions such as lack of privacy,
noise interference or a time clash with the medical follow-up, such as having the same
follow-up times for two potential interviewees at different hospitals or clinics.
Diagnosis validation by psychiatrist. In this study, a nurse researcher has undertaken
all depression assessments, though a psychiatrist or psychologist may be used in some
studies. Even though this study does not aim at diagnosis for depression, a validation
study having a single-blind consultation with a psycho-geriatrician (PG) was arranged.
All the screened positive subjects (depressed group) at six months and 20% of the
non-depressed group, randomly selected, attended the study. Because of the
geographical location, participants recruited from the study hospital were invited to
meet the psycho-geriatrician in another hospital, rather than staying at the same
hospital. Special arrangements for transfer between hospitals were necessary. Some
clashing of appointments (those of patients, researcher and PG) was encountered.
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Some consultations extended beyond three to six months rather than taking place
immediately after the second interview as planned. Seven out of the 20 depressed
subjects declined the interview with the psycho-geriatrician. The reasons given were
mainly travel sickness or the interview being perceived as redundant, as they were
already under medical or psychiatric care.
Missing data. Documentation on mood and some details of stroke lesion was not
complete in this dataset. Much of the recorded information on stroke lesions was brief,
seldom remarked their area, severity and location. More than half of the CT reports
were not available despite a routine CT scan for all stroke admissions. More often CT
results were denoted as ‘NAD’ (no abnormality detected). Useful anatomical and
biological data that might have yielded some understanding of the heated debate on
DAS etiology was not available. Some of the diagnoses had to be made on clinical
judgments by doctors who had jotted down very brief final decisions in the record or
discharge summary. The situation related to mood and depression in the case notes was
even worse. The assessment of mood was not incorporated as routine and therefore
only patients with overt signs and symptoms, including frequent crying or bizarre
behaviour, would be spotted by doctors or nurses. This is supported by the study
findings showing that only 19.7% (42 out of 214) of the participants reported being
asked about their mood during hospitalisation by healthcare professionals. As mood
assessment is not routine and may be conducted by different healthcare disciplines at
different times beyond the one-month interview, the direct use of the clinical data was
not adopted. Despite such limitations, we are able to clarify the laterality and major
conflicting documentation with the consultant of the stroke unit to enhance data
quality. In order to minimise missing data, the record study was done more than twice
for subjects with pending or unclear results.
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4.5 Recommendations for future research, practice and education
4.5.1 Future research
4.5.1.1 Incidence studies
The incidence in the current study presents very interesting results that require
continued follow-up to confirm the conflicting pattern (a falling or rising pattern). The
point that half the patients naturally resolve the depressive symptoms whereas the
remainder are likely to develop chronic depression (Andersen 1997) warrants another
study with a longer follow-up period. In this study, we selected a six-month follow-up
because of concerns over attrition and resources factors. Future studies involving
clinical partners may undertake a longer time frame for study (eg 1-3 years), also
recommended by previous studies (Dam 2001, Berg 2003). With more clinical
collaboration, recruitment of a control group indicating a prospective cohort design
will more likely to yield robust results. The significant drop in the current study may
also be due to a selection bias, as milder cases were recruited. But how the study result
may apply to those with a moderate or severe grade of stroke insult, as classified by a
standardised stroke inventory, is definitely worth further exploration. Updating the
above information on DAS is crucial for clinical care, scientific pursuance of
unanswered DAS etiology questions and for healthcare planning. More research effort
in these areas is also supported by the clinical outcome data of patients with DAS
presented in previous sections, as well as that set out under the topic ‘Significance of
current study’ in Chapter 1.
4.5.1.2 Bio-anatomical or biochemical research
The mechanism of DAS is an ongoing research topic as the root cause of DAS is
not fully understood because of methodological diversities (Aben et al. 2001). Some
advocate that DAS is simply a natural emotional response to the occurrence of any
debilitating disease. Evidence to support such an argument concentrates on
comparative studies to show that DAS is no more common than ordinary depression in
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older people in geriatric settings (Burvill et al. 1997), or compared with patients in
other disease group such as myocardial infarction (Aben et al. 2003) or lower limb
fractures (Mast et al. 1999) or other debilitating medical conditions (Evans & Whitney
1998). Another study uses a structural-equation modelling approach to examine
depression
symptom
endorsement
between
geriatric
stroke
patients
and
general-medical geriatric patients, indicating that stroke patients do not demonstrate
greater depression severity (Mast 2004). Whether such findings may also apply to
Chinese people deserves further exploration, as the data from the above research areas
cannot be assumed applicability in Chinese populations.
In bio-anatomical research, current studies are unable to find a strong laterality
effect or culprit structure for DAS such as the limbic region, because of missing data in
medical records. More vigorous research with the use of a brain bank and functional
MRI may be promising. There is suggestion that post-stroke pathological crying is
attributable to stroke-induced partial destruction of the serotoninergic raphe nuclei in
the brainstem or their ascending projections to the hemispheres (Andersen 1997).
By definition, vascular depression is that occurs in patients with cerebrovascular
or ischemic changes in the brain (Krishnan 2000). Stroke can be viewed as falling
along a continuum of potential cerebrovascular changes (ie, small vessel to large
vessel changes). It is possible that depression and ischemic stroke share a common
etiology and that the two conditions coexist (Leon et al. 1998). Researchers have
suggested examining the number of patients with vascular depression who may
develop stroke over time and determining if depression puts them at a greater risk than
those with cerebrovascular risk factors alone (Mast 2002). Others are keen to explore a
plausible marker for future stroke if late-onset depression has a vascular basis. In fact
some initial evidence has emerged to demonstrate that, disregarding age, gender and
ethnicity, depression was predictive of stroke (Jonas & Mussolino 2000), specifically
ischemic stroke among Japanese (Ohira et al. 2001). Such information is very limited
in Chinese populations.
In biochemical areas, studies have shown the protective effect of selective
serotonin re-uptake inhibitors (SSRIs) as a preventive measure to foster better
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treatment and functional outcomes (Herrmann 1999). Other reports suggest that both
tricyclics and SSRIs appear to be effective in improving DAS (Turner-Stokes &
Hassan 2002). But the reviews do not offer conclusive remarks on the relative efficacy
of SSRIs and heterocyclic anti-depressants nor prove a definitive protective effect
(Bhogal et al. 2005). This missing area also requires clinical trials to prove the efficacy
and safety of SSRIs in order to foster better quality of life among stroke survivors,
whose quality of life indices do not show much improvement at six months, after two
decades of discussion and concern in the field of stroke care.
Another emerging theory, the inflammatory model to explain stroke and
depression is a definite area for research. It may help explain the etiology of the
frequent comorbidity of depression and stroke in terms of common pathways and
involved biochemical components in the brains (Leonard 2006). Comorbidity of
dementia among the depressed group may be highly suspected though the temporality
and causality between stroke, dementia and depression in this sample are difficult to
prove. There is emerging evidence suggesting the changes in proinflammatory
cytokines occurring both in depression and dementia, implicating common etiology.
To study along the line, the use of cyclo-oxygenase (COX2) inhibitors might have
exerted both central anti-inflammatory changes as well as anti-depressant effect. This
is postulated from a clinical study result on patients resolving both osteoarthritis and
comorbid depression after a course of rofecoxib, COX2 inhibitors, explained by the
researcher (Leonard & Myint 2006). Therefore such explanation is biologically
plausible, extending the explanation to not just depression and stroke but also
dementia. This kind of research may provide very useful information that is definitely
a missing piece in Chinese populations.
4.5.1.3 Depression literacy studies
Health literacy studies will impact on wide areas of health, including those
involving communication, education and training in community, organisational and
policy development (Rootman & Ronson 2005). The current study shows that we may
still need to work very hard towards such a goal, so as to promote depression literacy
among older people and their families, since their knowledge, skill and motivation to
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learn about depression are still relatively low. The results of our study support the
supposition that older adults might not have adequate mental-health literacy as regards
decision-making. The majority of stroke patients aged 50+ reported inadequate
knowledge of depression, despite the fact that there was frequent coverage of
depression in the local news media. This certainly reflects the limitations of current
health-education programmes in reaching or fulfilling the needs of the target groups.
Clinicians tend to be unaware of or have unrealistic assumptions about patients’
reading abilities in health education. Worse still, none of the patient education material
that was studied considered cultural sensitivity to be an important factor (Wilson et al.
2003). Furthermore, the wide range of literacy levels within the same culture or among
various diversified ethnic groups is seldom investigated before any health promotion
attempts are made. Studies that examine and exhaust the verbal cues to be used by
older adults in expressing their emotional needs, pains and suffering are worthwhile if
curbing depression is a real healthcare goal or mental health promotion is an initiative
in the society.
4.5.1.4 Detecting depression
Current study acknowledges a very low detection and treatment rate of depressed
stroke patients when compared with European healthcare systems (Eriksson et al.
2004). The fact that the presence of depression limits the level of functional outcomes,
and the extension of rehabilitation potential among stroke patients, indicates that more
attention to the diagnosis and timely treatment of depression is needed to help make
therapeutic stimuli significant during rehabilitation (Battaglia & Bejor 2001). One
study recommends, given the prevalence and significance of depressive symptoms, the
active screening of all stroke patients together with aggressive management
(Herrmann 1999). Post-stroke depression also responds to anti-depressant treatment,
as reported by Jorge (2004), and also demonstrated in this study. The results actually
lend support to early detection and therefore encourage further investigation into the
development of a non-verbal assessment tool for depression and its reduction in
post-stroke patients (Kao 2000). Despite published work on a preliminary attempt to
use alternative means, smiley diagrams, to screen for depression (data published
already, see Publications), the sensitivity of the three emoticons is not very high and
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some truly depressed subjects may still be missed. And its utility in screening for
depression in the case of other chronic health conditions remains to be examined.
It has been argued that the proximity of stroke onset to DAS might have
implications for depression recognition and assessment (Tateno et al. 2002). The
selection of the periods (currently one and six months) for the two interviews that
reflect major events in the course of a disease has undergone a long process of
negotiation. Previous study periods ranged from the (1) the first week (Cassidy et al.
2004); (2) hospital discharge from stroke (Loong et al. 1995); (3) 3 months (Kauhanen
et al. 1999, Hayee et al. 2001, Popovich et al. 2002); (4) more than a year (Berg et al.
2003, Choi-Kwon et al. 2005). None of these studies has justified the time frame for
depression assessment. Only Carson has proposed that DAS can be a chronic
phenomenon since inception (Carson et al. 2000a). Thus there is no conclusive finding
on the best time to conduct depression assessments. A recent systematic review on
intervention efforts to prevent DAS analyses nine studies, most of which have
recruited stroke participants at one month after first stroke (Anderson et al. 2005).
Other research may involve the study of somatic symptoms, such as reduced
appetite, psychomotor retardation and fatigue, which are all found to have high
discriminative properties in DAS detection compared with the feelings of guilt or other
psychological symptoms such as hypochondriasis and lack of insight reported in
previous studies (de Coster et al. 2005). This research area may well be a fruitful one
as Chinese are known to somatise depressive symptoms (Woo et al. 1994).
4.5.2 Future practice and education
Education and practice go hand in hand. Current study results show a wide gap in
knowledge of DAS, and in the skills of detecting and treating DAS. A high incidence
of DAS among Chinese stroke survivors after the first episode at the acute phase and
after rehabilitation at six months has been identified. But active detection and
interventions for DAS lag very much behind many common cardiovascular conditions.
Current study findings also note the ignorance of DAS among the potential victims as
well as among the healthcare teams. It signifies a tremendous theory-practice gap.
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Ageism may be a root cause of under-investigation, and under-treatment of carotid
disease in elderly patients with transient ischaemic attack and stroke has been
documented (Fairhead & Rothwell 2006). More important, ageism has influenced the
public’s values and beliefs that might deter the detection and treatment of a complex
health problem - DAS among old-age populations. To combat such a pattern, we need
to start by fighting ageism in the first place during the education of healthcare
professionals.
The study regarding ageism in recognizing, detecting, diagnosing and treating
depression in older patients is worthwhile research area. Stereotypes and myths
regarding the inevitable emotional or cognitive impairment in late life have to be
clarified early across lifespan in all levels in the society. Studies may continue to look
into undergraduates’ learning needs and attitude regarding mental health literacy or
mental health promotion in particular depression in older adults as study has shown
that senior nursing students are less inclined to work with older people (Lee et al.
2006). At the end of the day, healthcare professionals play a significant role in
elucidating a correct concept of depression or DAS to the public. Healthcare input is
essential for differential diagnosis of DAS. Healthcare students and professionals have
to acknowledge the need of and be educated in aligning the language and literacy
levels of patients and their families to articulate the concept that ‘DAS is a medical
problem as common as cardiovascular diseases, as treatable using pharmacological
means and adjunct therapies as other medical diseases’. More information and
discussion of DAS may foster alertness and optimism in handling the problem by early
detection and prevention. Previous study has noted that early detection is prompted if
depressed patients may have tipped their mood verbally to healthcare professionals
(Robinson-Smith 2004).
To prompt early screening, the utility of three smiley pictures, a simple, brief and
non-verbal tool to assess depression among stroke survivors, has been proposed. It
may be a promising direction for future research and clinical applications when
healthcare staff are facing time constraints and competing demand on their services.
The role of healthcare professionals in fostering post-stroke quality of life has
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been discussed (Green & King, 2007) and found to be important as stroke survivors
have constantly suffered from low quality of life (Kim et al. 1999, Moon et al. 2004).
This study has updated current findings and affirmed static or poor quality of life
among a majority of stroke survivors with only mild to moderate levels of stroke.
Before the stress created by a sudden stroke onset turns into a chronic one that
aggravates the risk of depression as proposed by the literature (Leonard 2006),
effectiveness studies on measures to alleviate stress through activating the coping
mechanism among patients and families are also indicated. As an intervention, an
explanation of the natural course of DAS to patients and family care-givers helps to
allay worries and anxiety (Li et al. 2003), as the ‘worry about current health status’ at
one month is a predictor of depression at six months. Significantly more negative
cognitions and fewer positive cognitions were found in stroke patients who were
depressed than in stroke patients who were not (Nicholl et al. 2002). This is evidenced
in both the depression literacy results and by the smiley diagrams, being the
determinants or predictors in DAS. Patients with such a cognitive bias, cognitive
therapy can be an option (Lewis et al. 2001).
As patients’ self-care dependency is a potent determinant of DAS in both patients
and family care-givers (Clark & Smith 1999), practical material support is deemed
essential, in particular for the high-risk groups discussed in section 4.2.6. With only
finite resources, out-patient rehabilitation and support may be first directed towards
severely depressed patients and care-givers who are deprived of resources and other
forms of support (Kotila et al. 1998). Healthcare professionals need to take note of the
need to clarify multiple uncertainties regarding both stroke and DAS in terms of
disease etiology, recognition, prevention and intervention. This is particularly
important in the case of older Chinese adults who may have low formal education and
a reluctance to display emotions in front of non-family members (Bond 1991, Tung
1991), thus rendering useful verbal cues to prompt early detection of DAS in vain
(Robinson-Smith 2004).
Once a diagnosis of depression is made, treatment should be delivered promptly.
The treatment rate among local samples lies around 3.4%, and one in every ten DAS
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victims (11%). It is nearly the opposite scenario to that of other countries. Only 8.4%
of all depressed Swedish stroke patients are not treated with anti-depressants, such
medication being used by 22.5% of men and 28.1% of women who had had a stroke
(Eriksson et al. 2004). This is a definite area for improvement once robust
evidence-based data to support treatment efficacy in DAS has been established among
Chinese populations.
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Chapter 5 Summary and conclusions
This chapter is to summarize the findings, identify the added value of this
research, discuss the key the implications for research, practice and education, and,
lastly propose major areas for improvement.
The study of depression after stroke was the first of its kind when it was
conceptualised and commenced in the local territory. DAS is an important public
health topic. The victim has to bear a triple condition - depression, stroke and old age.
Inattention is unethical and is likely to pose a costly burden on patients, family and
healthcare services. Before active intervention, the clarification of problem intensity,
its nature and natural history in Chinese populations is justified. The aims of this study
were to identify the incidence of DAS for first-ever ischemic stroke patients in Hong
Kong, the determinants at both one month and six months, and the predictors that
might explain the six-month depression score. This formed the primary study outcome.
As secondary outcomes, the study addressed changes in the different domain factors
among stroke survivors, and their clinical outcomes. With the study results, it becomes
possible to rationalise interventions that are also supported in the literature.
This study updates and adds information to address several unresolved research
questions, in particular those involving Chinese populations. It identifies a high
incidence of depression among older stroke patients: one in four at one month and one
in ten at six months. DAS is thus common among Chinese, comparable to levels
documented in other countries. The determinants and predictors of depression after
stroke (DAS) at acute and chronic stages have been explored and discussed. Factors in
five domains have been examined - bio-anatomical, cognitive and communicative,
dependency and somatic symptoms, emotional, and family and social support. In line
with previous studies, factors in all five domains are found to be associated with DAS.
To estimate DAS, two assessment tools have been used on top of the Diagnostic
and Statistical Manual IV for major depression (DSM), the gold standard, to conduct
interviews (by a senior research nurse) with ischemic stroke patients. The incidence of
depression after stroke identified by DSM was 29% at one month and dropped to 11%
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at six months. A slightly lower incidence was obtained by using CESD and GDS. This
result was still comparable with previous data, which ranged between 10 and 40%.
Some mismatch in the study results is also anticipated with a variety of different
assessment tools being adopted in the assessment of depression, and inherent cultural
differences are expected, in particular mental-health literacy and emotion assessments
which are uncommon in acute medical settings.
The results of this study also support an increased effort to acknowledge DAS
using an evidence-based approach. The determinants for DAS at one month fall into
the emotional and dependency-level domains. The role of cognitive function became
more prominent at six months and was identified together with the other two domain
factors at one month, providing evidence to confirm the complex nature of DAS.
Despite the frequent belief in family support as a buffering factor in DAS, this study
had found its effect to be much overrated. To be able to identify DAS in high-risk
groups, due attention should be paid to cognitive function, disturbance due to memory
change, dependency and somatic symptoms, as well as the presence of an upset/tearing
facial expression and multiple worries, so that preventive work or constant monitoring
can be instituted.
Another insight about the predictors of DAS involves somatic symptoms. This is
particularly relevant to older Chinese adults who are known to somatise, thus making
detection of depression less likely. Lingering but unresolved somatic symptoms, as
simple as constipation, may render patients depressed. Healthcare professionals can
explore the intensity and duration of existing somatic problems as well as monitoring
the effects of treatment. With proper monitoring of these domain factors, early
interventions can be initiated to prevent profound disease debilitation.
Apart from answering the three most important research questions, as set out
earlier, the study results also offer new and additional utility in the following areas: (1)
depression literacy of stroke survivors and (2) smiley diagrams to prompt preliminary
screening of DAS.
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Using elderly after-stroke Chinese patients as a study population, our paper
represents a preliminary exploration into mental health literacy levels and the
expression of depressed moods, cognition or behaviour. In this study, the definition of
depression provided by the stroke patients does not match that used by clinicians. Our
results have provided some insight into future work in mental health promotion among
the older population, irrespective of ethnicity. As health literacy is critical to
empowerment (Nutbeam 1998) and the provision of information does not guarantee
desirable health literacy to aid effective decisions on depression, healthcare educators
need to rethink carefully in designing health communication strategies for older adults
in promotion and education programmes (Vahabi 2007). Future research may explore
existing initiatives, clarify the extent of problems in health literacy among different
target groups in culturally appropriate contexts, particularly during the assessment and
planning phases of health promotion and education programmes, in order to justify
outcome-based, cost-effective and culturally specific studies.
Health literacy is a primary core research element or component as highlighted in
this study. The current study shows that we may still need to work very hard towards
such a goal, so as to promote depression literacy among older people and their families,
since their knowledge, skill and motivation to learn about depression are still relatively
low. To raise depression literacy, the joint efforts of healthcare authorities, clinicians
and older adults are needed to be effective. Health literacy studies will impact on wide
areas of health, including that involving communication, education and training in
community, organisational and policy development (Rootman & Ronson 2005). This
is supported by a previous finding of the indispensable roles that medical
societies/associations and health politics in assuring the provision of adequate service
to tackle depression in later life (Bramesfeld 2003).
It is sad to note that only a small number of the depressed patients (11%) were
given due attention by healthcare facilities, suggesting serious under-recognition and
under-detection of DAS in local acute settings.
In Hong Kong, there is also a paucity of evidence supporting any particular tool
as superior in measuring DAS among local Chinese subjects. The search for a simple
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but valid tool to detect depression has attracted much attention over the last decade
(Anderson 1996, Watkins et al. 2001, Arroll 2003). This study reports on our use of
simple smiley diagrams to help in the early detection of DAS. We have also examined
their compatibility with the gold standard and Geriatric Depression Scale (GDS), as
well as their merits when clinically applied.
A simple, non-language-based, culturally neutral, non-verbal tool is desirable that
is easy to apply and not highly dependent on training. It may help front-line healthcare
workers, particularly nursing staff, to identify depression early in that particular
patient population. The smiley pictures seem to fulfil these requirements for early and
prompt screening of older patients. Information deriving from this study may assist
researchers and clinicians who are keen to identify a valid but economical tool for
prompt DAS assessment (Watkins et al. 2001, Arroll 2003, 2005). While families and
clinicians may take worries and a sad face to be a natural reaction to stroke after one
month, their presence and persistence are indicators of mental health concerns and
worth close monitoring. The current study offers some preliminary findings on the
utility of the culturally universal, non-verbal and less literacy-dependent diagrams to
prompt early screening and monitoring for busy clinicians in stroke or medical units. If
the tool is further developed and passes vigorous psycho-metric testing in the future, it
will reduce numerous barriers to the early detection of DAS faced by geriatricians and
patients.
DAS sufferers are vulnerable as they may be either depression illiterate or unable
to express their pleas for attention and help in appropriate words. Healthcare
professionals should be proactive and the first to help clarify the presence of DAS and
alter the diagnosis, thus offering interventions when DAS is masked by stroke-related
symptoms. They should raise the index of suspicion in the case of high-risk groups,
prompt early assessment, continue monitoring, educate patients and relatives in the
common signs and symptoms of DAS, and initiate relevant treatments in partnership
with the family. The professional’s attitude, knowledge and skill in dealing with DAS
are essential to relieve the multiple burdens it may cause.
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DAS is a complex but important public health problem. Under-recognition,
under-detection and under-treatment are all common, as observed in current findings,
which indicate the need for a coordinated effort ranging from detection to diagnosis
and treatment of DAS. Moreover, the needs and treatment regimes of Chinese
depressed stroke patients are not fully understood. Assessment and the emerging and
growing diversity of treatment suggested in other countries remain an ongoing
research agenda, in particular when cultural sensitivity and relevance in emotional
care are highly desirable but not readily available. Such a trend will impose a
tremendous burden and high costs on patients, families and healthcare services.
However, DAS is a topic less frequently researched in Chinese populations, despite
the high inherent risks. Several reasons may be proposed: (1) DAS is a three-in-one
condition which is likely to have a complex etiology and a diversity of clinical
outcomes; (2) inconsistent evidence in the literature does not help to provide a
confident platform for unanimous research results; (3) stroke may be considered of
higher clinical importance than depression when conflicting results of DAS are
common; (4) there is likely to be a taboo among Chinese on talking about emotions
or mental health, and data collection may therefore be difficult; (5) ageism in clinical
care is common, as emotional nuisance is inevitable or at least common in later life.
The justification for assuring recognition, early detection and interventions for
patients with this triple burden is reflected in the difference in clinical outcomes
between depressed stroke patients and their counterparts in this study and that in the
literature. The former group has a significantly higher hospitalization due to other
health problems other than stroke, lowered level of post-stroke quality of life and a
higher mortality at six months. Untreated DAS is costly to stake-holders, as this
study shows.
An important message emerges for front-line clinicians in clinical practice.
Despite rehabilitation efforts and decades of research into stroke patients, the quality
of life in the majority of patients is low at one month after stroke and remains so
without change at six months. The assessment of mood (including worries) should be
administered early to salvage a compromised quality of life among stroke survivors
over the long term. Clinicians may need to monitor factors falling into the cognitive,
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emotional and physical domains, as these are modifiable towards positive change.
This study attempts to show how an intricate physical-mental health complex can
be delineated and studied. It also provides some basic information for comparison in
future research. There is much room for improvement. Suggestions are made here on
ways to improve the quality of data in current study, in future research and in clinical
practice with respect to DAS in Chinese populations. To yield a more representative
sample, a prospective cohort design or a probabilistic sample from a representative
sampling frame is preferable. Some of the measurement tools perhaps need to undergo
more vigorous pre-testing for psychometric properties, in particular those
self-developed items in the somatic domain together with a feasibility test in the pilot
study.
As noted by previous researchers, many unresolved questions or inconclusive
findings will call for more research (Toso et al. 2004), addressing complex disease
pathogenesis (Whyte & Mulsant 2002), in particular the causality between depression
and stroke; whether they share similar mechanisms in a continuum of cerebro-vascular
basis is a classic research topic (Provinciali & Coccia 2002, Dieguez et al. 2004).
Despite the long list of research possibilities in Section 4.5, encompassing both
broad directions and specific areas for future study, research in the case of Chinese
populations should be state-of-the-art and target stroke survivors’ and families’
practical needs. Dissemination of robust study results for clinical practice has to be
addressed. More important, the theory-practice gap between academic institutes and
clinical facilities has to be spotted and bridged. Genuine interdisciplinary
collaboration also poses a vital component for meaningful effort in early recognition
and detection for effective intervention as a recent review has rightly pointed out that
the assessment of depression is unable to change patients’ clinical outcome in terms of
treatment prescription by physicians (Gilbody et al. 2006).
180
What is already known about DAS?
z
There is no unanimous pathogenesis model regarding Depression after
stroke (DAS)
z
Depression after stroke (DAS) is common in Caucasian samples
z
Unresolved DAS poses triple burden to patient, families and healthcare
z
DAS might impact on rehabilitation and clinical outcomes
z
Depressed stroke survivors suffer a compromised quality of life
z
The extent of problems in health literacy among Chinese populations is not
known
z
Detecting depression in elderly patients is important but often difficult
because of factors relating to patients, healthcare professionals and
assessment tools
z
A valid and user-friendly assessment tool is required for early and prompt
screening of geriatric depression and DAS in busy medical settings
181
What this study adds?
z
DAS is as common among older Chinese stroke survivors as the Caucasian
populations
z
DAS occurs one in four at one month and one in ten at six months
z
Under-recognition, under-detection and under-treatment of DAS are all
common in Chinese stroke patients
z
The quality of life among two thirds of older Chinese stroke survivors is
unchanged or lower at six months
z
Life stressors, cognitive impairment and display of a sad face at one month
predict depression score, and jointly explain a 44% variability of DAS at
six months
z
Older Chinese stroke survivors have relatively low literacy level with
respect to depression and seldom regard depression as a medical disease
z
Careful interpretation of DAS assessment results is needed because of
cultural specificity in verbalizing and moderating emotions among Chinese
z
The degree of agreement between DSM IV and the ‘sad’ emoticon is
moderately high and comparable with that of the Geriatric Depression
Scale (Chinese)
z
Smiley diagrams, being a simple, non-language-based, culturally neutral,
less time-consuming tool that is also easy to apply, might be suitable for
preliminary screening of DAS
z
Depressed stroke patients have minimal access to formal mental healthcare
z
Among
those
identified
depressed
and
prescribed
with
oral
anti-depressants, they are not classified as depressed at six months any
more
z
Many psycho-social issues detected in the study contribute useful
reference for future healthcare planning, researches, practice and education
in local settings
z
Proper dissemination of robust study results for clinical practice has to be
addressed
182
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Appendices
Appendix 2.1 Timetable for the research project
Appendix 2.2 DAS consent form
Appendix 2.3 Interview guide for psychogeriatrician
Appendix 2.4 1M questionnaire
Appendix 2.5 6M questionnaire
Appendix 2.6 Comparison of age and gender between two target hospitals
Appendix 3.1 Bio-chemical status of participants at 1M after stroke admission
Appendix 3.2 Individual item score in DSM at 1M and 6M
Appendix 3.3 Individual item score in GDS at 1M and 6M
Appendix 3.4 Individual item score in CESD at 1M and 6M
Appendix 3.5 Correlation of risk factors with GDS at 1M and 6M
Appendix 3.6 Correlation of risk factors with CESD at 1M and 6M
202
Appendix 2.1
Timetable for the research project
Research tasks
Duration (Wks)
Period
4
Nov 03
12
Nov 03 - Jan 04
Pilot Phase
Preparation of proposal and consultation with supervisors
Literature review
Set questionnaire
Pilot testing of questionnaire
Revision of questionnaire
6
6
2
Second pilot testing of questionnaire
4
Final revision of questionnaire
2
Application of ethics approval for study from hospitals
Liaison with hospital for study arrangement (time, place, person, material)
Jan 04 - May 04
26
Nov 03 - Apr 04
20
Dec 03 - Apr 04
56
Apr 04 - Mar 05
56
May 04 - May 05
52
May 04 - May 05
56
May 04 - May 05
52
Jun 04 - May 05
12
Jul 04 - Sep 04
26
Oct 04 - Mar 05
16
Dec 04 - Mar 05
2
Sep 04
2
Sep 04
8
Oct 04 - Nov 04
26
Oct 04 - Mar 05
36
Nov 04 - Jul 05
2
Aug 05
26
Jun 05 - Nov 05
12
Dec 05 - Feb 06
26
May 06 - Nov 0 6
8
Nov 06 - Dec 06
2
Dec 05
at wards, record office, clinics
First month (1M) interview
Case screening and recording exclusion list
Obtain consent from potential subjects
Affirmation of follow up date
Retreival of clinical information and data entry
Undertaking interview at 1M
Data management-entry, checking, cleaning, analysis
Writing interim report and prepare the first paper
Presentation in media, seminars and conference
Six month (6M) interview
Set FU questionnaire
Pilot testing of FU questionnaire
Record study for missing data at 1M, trace FU details
Phone confirmation for 6-month follow-up
Undertaking interview after 6M (clinics, home, age home, hospitals)
Retreival of records after 6M interview to check mood status and use of
drugs
Update literature review
Data management for 6M data
Prepare the second and third papers
Presentation in seminars
Validation study with psychogeriatrician
Prepare and pilot the consultation form
203
Arrange travel of patients and scheduling of consultation
Data management for consultation form
26
Dec 05 - May 06
12
Apr 05 - Jun 06
8
Jul 06 - Aug 06
12
Jun 06 - Aug 06
12
Sep 06 - Nov 06
2
Dec 06
26
Dec 06 - May 07
8
Jun 07 - Oct 07
4
Nov 07
Thesis write-up
Record retrieval for final checking of missing data
Data management
Revision of submitted papers
Consultation with statistician for data analysis and presentation
Draft an outline for the write up & submit the first draft by May 07
Second draft submission
Thesis (final) submission
204
Appendix 2.2
DAS consent form
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
Appendix 3.1
Bio-chemical status of participants at 1M after stroke admission
n
Mean
(Al-Shahi)
Min-Max
195
5.3 (1.4)
1.0-9.6
202
7.1 (2.8)
1.1-18.6
177
1.5 (1.1)
0.5-8.1
79
1.2 (0.6)
0.8-4.0
197
3.5 (1.0)
0.9-6.8
123
1.3 (0.3)
0.5-2.3
203
149.6 (25.3)
100-233
202
80.0 (13.8)
47-128
Blood result
Total Cholesterol level
Fasting blood sugar level
Triglyceride level
INR
Low Density Lipoprotein level
High Density Lipoprotein level
Systolic BP
Diastolic BP
Drug prescription, n (%)
Aspirin
Anticoagulant
Statin
Anti-hypertensive drug
Diabetic drug
Others (e.g. antibiotic, PPI)
180
(84.9)
47
(22.2)
139
(65.6)
104
(49.1)
57
(26.9)
148
(69.8)
222
Appendix 3.2
Individual item score in DSM at 1M and 6M
Proportion reported ‘Yes’
z
Depressed mood most of the day or nearly everyday
z
Marked decrease in interest or pleasure in all or almost all
basic living most of the day or nearly everyday
z
Increase or loss in appetite or body weight more than 5
percent
z
Insomnia or hypersomnia nearly everyday
z
Psychomotor retardation or agitation nearly everyday
z
Fatigue or loss of energy nearly everyday
z
Feelings of worthlessness or excessive or inappropriate guilt
z
Diminished ability to think or concentrate or indecisiveness
nearly everyday
z
Recurrent thoughts of death or suicidal thoughts or suicidal
plans or attempts or commit suicide
1-Month
(n=214)
6-Month
(n=188)
n
%
n
%
77
36.0
42
19.6
40
18.7
17
7.9
35
16.4
14
6.5
66
30.8
35
16.4
77
36.0
52
24.3
100
46.7
74
34.6
82
38.3
55
25.7
47
22.0
10
4.7
52
24.3
23
10.7
223
Appendix 3.3
Individual item score in GDS at 1M and 6M
Proportion reported ‘Yes’
GDS 1 Basically satisfied with life
GDS 2 Dropped many of activities and interest
GDS 3 Feel that life is empty
GDS 4 Often get bored
GDS 5 In good spirit most of the time
GDS 6 Afraid that something bad is going to happen
GDS 7 Feel happy most of the time
GDS 8 Often feel helpless
GDS 9 Prefer to stay at home rather than going out and doing new
things
GDS 10 Feel having more problems with memory than most
GDS 11 Think it is wonderful to be alive now
GDS 12 Feel pretty worthless the way you are now
GDS 13 Feel full of energy
GDS 14 Feel that your situation is hopeless
GDS 15 Think that most people are better off than you are
1-Month
(n=214)
6-Month
(n=188)
n
%
n
%
50
23.4
28
14.9
121
56.5
64
34.0
56
26.2
28
14.9
100
46.7
69
36.7
87
40.7
56
29.8
64
29.9
33
17.6
88
41.1
49
26.1
63
29.4
42
22.3
67
31.3
27
14.4
48
22.4
23
12.2
48
22.4
29
15.4
91
42.5
47
25.0
107
50.0
61
32.4
64
29.9
39
20.7
52
24.3
22
11.7
224
Appendix 3.4
Individual item score in CESD at 1M and 6M
Proportion reported ‘Yes with 1, 2, 3’
CESD 1 I was bothered by things that usually don’t
1-Month
(n=214)
1
2
3
CESD 2 I felt depressed
1
2
3
CESD 3 I felt fearful
1
2
3
CESD 4 My sleep was restless
1
2
3
CESD 5 I could not get going
1
2
3
CESD 6 I felt that thing I did was an effort
1
2
3
CESD 7 I felt lonely
1
2
3
CESD 8 I had trouble keeping my mind on what I
was doing
1
2
3
CESD 9 I felt trouble about the future
1
2
3
CESD 10 I was happy
1
2
3
6-Month
(n=188)
N
%
N
%
26
12.1
49
26.1
15
7.0
19
10.1
37
17.3
10
5.3
25
11.7
51
27.1
32
15.0
31
16.5
38
17.8
12
6.4
25
11.7
33
17.6
10
4.7
17
9.0
25
11.7
8
4.3
38
17.8
51
27.1
20
9.3
19
10.1
34
15.9
14
7.4
38
17.8
38
20.2
20
9.3
18
9.6
21
9.8
4
2.1
35
16.4
59
31.4
34
15.9
31
16.5
47
22.0
13
6.9
34
15.9
29
15.4
19
8.9
21
11.2
28
13.1
20
10.6
41
19.2
17
9.0
16
7.5
8
4.3
12
5.6
2
1.1
50
23.4
34
18.1
18
8.4
18
9.6
37
17.3
21
11.2
56
26.2
58
30.9
33
15.4
28
14.9
50
23.4
18
9.6
0=Rarely or none of the time; 1=Some or a little of the time 1 to 2 days a week; 2=Occasionally or a moderate
amount of the time 3 to 4 days a week; 3=Most or all the time 5 to 7 days a week
225
Appendix 3.5
Correlation of risk factors with GDS at 1M and 6M
1M
6M
r
r
Physical domain
BADL sum score (0-20)
a
-0.497***
-0.428***
-0.546***
--
0.439***
0.518***
headache (0-3)
0.288***
0.169*
stomach pain (0-3)
0.168*
joint pain (0-3)
0.153*
Norton Scale of pressure sore sum score (5-20)
Modified Rankin Scale for Quality of Life (0-5)
b
Level of disturbance due to
non-specific muscle pain (0-3)
0.196**
0.265***
dizziness (0-3)
0.210**
0.151*
constipation (0-3)
0.204**
0.205**
other specific health problems (0-3)
Total number of body symptoms (0-10)
0.172*
c
0.290***
0.364***
-0.307***
-0.419***
Cognitive and Communicative domain
Sum score Abbreviated Mental Test
d
Expressive aphasia (0-3)
0.219**
Global aphasia (0-3)
0.170*
Dysarthria (0-3)
0.233***
Swallowing problems (0-3)
0.145*
Visual acuity problems (0-3)
0.232***
0.221**
Memory problems (0-3)
0.204**
0.145*
226
1M
6M
r
r
Emotional domain
Perceived current health status (1M=1-5; 6M=1-3)
0.433***
0.541***
Level of worry about current health (0-3)
0.502***
--
a pleasant facial expression (0-3)
-0.415***
-0.510***
a flat/average facial expression (0-3)
0.288***
0.229**
0.478***
0.600***
Reported depression (0-1)
0.362***
--
Reported worry (0-1)
0.203**
--
Reported panic (0-1)
0.156*
--
Reported anxiety (0-1)
0.149*
--
Reported agitation (0-1)
0.171*
--
0.294***
0.216**
stroke rehabilitation progress (0-3)
0.483***
0.344***
financial problem (0-3)
0.279***
0.210**
bodily pain and discomforts (0-3)
0.305***
0.259***
poor family relationship (0-3)
0.214**
Days in the last week with
an upset facial expression or cry (0-3)
Types of emotions dissipated during hospitalization
e
Level of disturbance due to anxiety (0-3)
Worries over
losing job/loss of job (0-3)
0.211**
f
0.500***
0.515***
Finding someone to chat (0-3)
0.057
--
Going for a walk (0-3)
-0.083
--
Having exercise (0-3)
0.105
--
--
-0.334***
--
-0.049
--
-0.232**
--
-0.343***
Total number of worries
Social support domain
Usefulness of means to relieve unhappiness at one month
Sum score of Lubben social network scale (LSNS) at six month
Subtotal score of LSNS on family
Subtotal score of LSNS on relatives
Subtotal score of LSNS on confidant
a-f
Spearman's rho test was used
Pearson correlation test was used
* Significant at <0.05
** Significant at <0.01
*** Significant at <0.001
227
Appendix 3.6
Correlation of risk factors with CESD at 1M and 6M
1M
6M
r
r
Physical domain
BADL sum score (0-20)
a
-0.475***
-0.382***
-0.506***
--
0.426***
0.443***
headache (0-3)
0.275***
0.202**
stomach pain (0-3)
0.168*
0.159*
joint pain (0-3)
0.153*
0.251***
Norton Scale of pressure sore sum score (5-20)
Modified Rankin Scale for Quality of Life (0-5)
b
Level of disturbance due to
low back pain (0-3)
0.161*
non-specific muscle pain (0-3)
0.243***
dizziness (0-3)
0.229***
0.248***
constipation (0-3)
0.184**
0.232**
other specific health problems (0-3)
Total number of body symptoms (0-10)
0.144*
c
0.281***
0.412***
-0.327***
-0.371***
Expressive aphasia (0-3)
0.147*
0.150*
Global aphasia (0-3)
0.145*
Dysarthria (0-3)
0.195**
Visual acuity problems (0-3)
0.286***
Memory problems (0-3)
0.141*
Cognitive and Communicative domain
Sum score Abbreviated Mental Test
d
0.218**
228
1M
6M
r
r
Emotional domain
Perceived current health status (1M=1-5; 6M=1-3)
0.388***
0.535***
Level of worry about current health (0-3)
0.563***
--
a pleasant facial expression (0-3)
-0.405***
-0.496***
a flat/average facial expression (0-3)
0.305***
0.215**
0.568***
0.561***
Reported depression (0-1)
0.426***
--
Reported worry (0-1)
0.213**
--
Reported panic (0-1)
0.189**
--
Reported anxiety (0-1)
0.112
--
Reported agitation (0-1)
0.179**
--
0.338***
0.277***
stroke rehabilitation progress (0-3)
0.511***
0.333***
financial problem (0-3)
0.217**
0.238**
bodily pain and discomforts (0-3)
0.318***
0.281***
disease deterioration (0-3)
0.368***
poor family relationship (0-3)
0.162*
Days in the last week with
an upset facial expression or cry (0-3)
Types of emotions dissipated during hospitalization
e
Level of disturbance due to anxiety 0 to 3
Worries over
losing job/loss of job (0-3)
0.234**
other things (0-3)
Total number of worries
0.204**
f
0.460***
0.549***
Finding someone to chat (0-3)
0.113
--
Going for a walk (0-3)
-0.067
--
Having exercise (0-3)
0.179**
--
--
-0.325***
--
-0.088
--
-0.233**
--
-0.287***
Social support domain
Usefulness of means to relieve unhappiness at one month
Sum score of Lubben social network scale (LSNS) at six months
Subtotal score of LSNS on family
Subtotal score of LSNS on relatives
Subtotal score of LSNS on confidant
a-f
Spearman's rho test was used
Pearson correlation test was used
* Significant at <0.05
** Significant at <0.01
*** Significant at <0.001
229