Literature Review - Psychology
follow all directions
DEBATE Open Access
Depression and anxiety among people
living with and beyond cancer: a growing
clinical and research priority
Claire L. Niedzwiedz1* , Lee Knifton2,3, Kathryn A. Robb1, Srinivasa Vittal Katikireddi4 and Daniel J. Smith1
Abstract
Background: A cancer diagnosis can have a substantial impact on mental health and wellbeing. Depression and
anxiety may hinder cancer treatment and recovery, as well as quality of life and survival. We argue that more
research is needed to prevent and treat co-morbid depression and anxiety among people with cancer and that it
requires greater clinical priority. For background and to support our argument, we synthesise existing systematic
reviews relating to cancer and common mental disorders, focusing on depression and anxiety.
We searched several electronic databases for relevant reviews on cancer, depression and anxiety from 2012 to 2019.
Several areas are covered: factors that may contribute to the development of common mental disorders among
people with cancer; the prevalence of depression and anxiety; and potential care and treatment options. We also
make several recommendations for future research. Numerous individual, psychological, social and contextual
factors potentially contribute to the development of depression and anxiety among people with cancer, as well as
characteristics related to the cancer and treatment received. Compared to the general population, the prevalence
of depression and anxiety is often found to be higher among people with cancer, but estimates vary due to several
factors, such as the treatment setting, type of cancer and time since diagnosis. Overall, there are a lack of high-
quality studies into the mental health of people with cancer following treatment and among long-term survivors,
particularly for the less prevalent cancer types and younger people. Studies that focus on prevention are minimal
and research covering low- and middle-income populations is limited.
Conclusion: Research is urgently needed into the possible impacts of long-term and late effects of cancer
treatment on mental health and how these may be prevented, as increasing numbers of people live with and
beyond cancer.
Keywords: Mental health, Psychiatry, Cancer, Multimorbidity, Depression, Anxiety, Oncology, Survivorship
Background
A cancer diagnosis can have a wide-ranging impact on
mental health and the prevalence of depression and anx-
iety among people with cancer is high [1, 2]. Among
those with no previous psychiatric history, a diagnosis of
cancer is associated with heightened risk of common
mental disorders, which may adversely affect cancer
treatment and recovery, as well as quality of life and sur-
vival [3]. People who have previously used psychiatric
services may be particularly vulnerable and at greater
risk of mortality following a cancer diagnosis [4]. How-
ever, the mental health needs of people with cancer, with
or without a prior psychiatric history, are often given lit-
tle attention during and after cancer treatment, which is
primarily focused on monitoring physical health symp-
toms and side effects. Advances in the earlier detection
of cancer and improved cancer treatments means that
people are now living longer with cancer, presenting a
significant global challenge. The total number of people
who are alive within 5 years of a cancer diagnosis was
estimated to be 43.8 million in 2018 for 36 cancers
across 185 countries [5], and in the United States alone,
the number of cancer survivors is projected to rise
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: [email protected]
1Institute of Health & Wellbeing, University of Glasgow, Glasgow, Scotland,
UK
Full list of author information is available at the end of the article
Niedzwiedz et al. BMC Cancer (2019) 19:943
https://doi.org/10.1186/s12885-019-6181-4
exponentially from 15.5 million in 2016 to 26.1 million
in 2040 [6].
The main objective of this article is to argue that more
research is needed into the prevention, care and treat-
ment of co-morbid depression and anxiety among
people with cancer and highlight it as a growing clinical
and policy priority. For background and to support our
argument, we provide a current evidence review of sys-
tematic reviews relating to common mental disorders
amongst people living with and beyond cancer. We
cover the factors that may increase the risk of experien-
cing co-morbid depression and anxiety, epidemiology,
and potential care and treatment options.
We searched three key electronic databases: Medline,
PsycINFO and CINAHL (Cumulative Index to Nursing
and Allied Health Literature) for relevant reviews (favour-
ing those using systematic methods) using the following
search terms: (neoplasm OR carcinoma OR tumo*r OR
cancer) AND (depression OR anxiety) AND review. Only
English language articles were considered and searches
were limited to the years 2012 to 2017 and updated during
February 2019. These years were considered adequate to
capture the main themes relating to cancer and common
mental disorders in the current literature. The references
of highly relevant articles were scrutinised for additional
papers and a Google search for important grey literature
was also conducted. A minority of significant research ar-
ticles known to the authors were also consulted.
Main text
Factors influencing the development of depression and
anxiety among people with cancer
A variety of factors are likely to interact to influence the
development of depression and anxiety among people
with cancer (summarised in Fig. 1), but these are not well
understood [1], and require further research. Individual
Fig. 1 Factors that may contribute to depression and anxiety among people living with and beyond cancer
Niedzwiedz et al. BMC Cancer (2019) 19:943 Page 2 of 8
risk factors that may increase the risk of depression, simi-
lar to the general population, include demographic factors,
such as age and gender, and social and economic factors
such as unemployment, fewer educational qualifications
and a lack of social support [7]. The development of de-
pression and anxiety among people with cancer is also
likely to depend on factors at the structural level, includ-
ing healthcare costs and access, as well as access to welfare
support, such as disability benefits, as cancer can have a
significant financial impact [8, 9]. Several psychological
factors are also important. A key factor is the presence of
pre-existing mental health problems and their severity.
Research has demonstrated that individuals who have
previously accessed mental health services before a cancer
diagnosis experience excess mortality due to certain
cancers, which may reflect late diagnosis, inadequate
treatment and a higher rate of adverse health behaviours
[4, 10]. Personality factors, such as neuroticism, and exist-
ing coping skills may also contribute [11]. The risk of
suicide among people with cancer is higher than the gen-
eral population for certain diagnoses that tend to have
poorer prognoses, such as mesothelioma and lung cancer,
especially in the first 6 months after diagnosis [12, 13].
Individuals who have previously engaged in suicidal be-
haviour are likely to be particularly vulnerable.
The individual psychological response to a cancer
diagnosis is also likely to be an important component.
The experience of being diagnosed, particularly if the
diagnosis has been delayed, can be a significant source
of distress and can impact on illness acceptance [14].
Feelings of hopelessness, loss of control and uncertainty
around survival and death can also have a detrimental
impact, particularly in patients with a poor prognosis.
Anxiety around a cancer diagnosis can also lead to sleep
disturbance, which may increase the risk of depression
[15]. The stigma surrounding both mental illness and
certain types of cancer, such as lung cancer, can lead to
feelings of guilt and shame, which could contribute to
the onset of depression. For example, the link between
smoking and lung cancer can lead to some patients
blaming themselves for their illness and experiencing
stigma if they have engaged in smoking [14].
A variety of factors related to the cancer and its treatment
are likely to impact on the development of depression and
anxiety, including the type of cancer, stage and prognosis.
Cancer treatments including immunotherapy and chemo-
therapy may induce depression through particular biological
mechanisms, such as inflammatory pathways, and some
medications used to treat chemotherapy-induced nausea
can reduce dopaminergic transmission, which is implicated
in the development of depressive symptoms [16]. The use of
steroids in cancer treatment can induce depression [17], and
androgen deprivation therapy in the treatment of prostate
cancer is also associated with increased risk [18]. The
physical symptoms of specific cancers can also contribute to
depression (e.g. incontinence and sexual dysfunction associ-
ated with prostate cancer) [19]. Iatrogenic distress is also
commonly reported amongst patients, which could increase
the risk of experiencing later problems with depression and
anxiety, including post-traumatic stress disorder [20]. This is
often related to a combination of poor communication, a
lack of consideration of psychological concerns and dis-
jointed care [14, 20].
Prevalence of depression and anxiety among people with
cancer
The prevalence of common mental disorders among
people with cancer varies widely in the published litera-
ture. The mean prevalence of depression using diagnos-
tic interviews is around 13\% and using all assessment
methods it varies from approximately 4 to 49\% [2, 21].
This wide variation is due to several factors including
the treatment setting, type of cancer included and
method used to screen for symptoms (e.g. interview by
trained psychiatrist or self-report instrument). The esti-
mated prevalence of depression was found to be 3\% in
patients with lung cancer, compared to 31\% in patients
with cancer of the digestive tract, when diagnostic inter-
views were used [21]. A meta-analyses of 15 studies
meeting a number of quality criteria, including the use
of diagnostic interviews, found that the estimated preva-
lence of depression varied across treatment settings (5 to
16\% in outpatients, 4 to 14\% in inpatients, 4 to 11\% in
mixed outpatient and inpatient samples, and 7 to 49\% in
palliative care) [2]. There is no universal standardised tool
which is recommended for depression screening in patients
with cancer and the method used is likely to differ depend-
ing on the treatment setting. A meta-analysis of screening
and case finding tools for depression in cancer settings iden-
tified 63 studies that used 19 different screening tools for
depression [22]. Common screening methods for depression
include semi-structured diagnostic interviews, the Hospital
Anxiety and Depression Scale - depression subscale (HADS-
D) and Center for Epidemiologic Studies Depression Scale
(CESD), which are designed to measure the severity of
depressive symptoms.
An important aspect that needs to be considered is the
timing of increased psychiatric risk. Studies demonstrate
that depression tends to be highest during the acute
phase and decreases following treatment, but again this
likely differs depending on the type of cancer and prog-
nosis [21]. Using diagnostic interviews, the prevalence of
depression during treatment was found to be 14\%, 9\% in
the first year after diagnosis and 8\% a year or more after
treatment in a meta-analysis of 211 studies [21]. Of the
238 cohorts included, around 30\% included only breast
cancer patients and there is a need for research includ-
ing rarer types of cancer.
Niedzwiedz et al. BMC Cancer (2019) 19:943 Page 3 of 8
As well as the type of cancer, the type of mental health
outcome considered is also important and fewer studies
have examined anxiety. A systematic review and meta-
analysis study focusing on patients with ovarian cancer
found that anxiety tended to be higher following treatment
(27\%) and during treatment (26\%), and was lowest pre-
treatment (19\%) [23]. The heightened anxiety observed
post-treatment may be due to reduced clinical consultations
and support following treatment, potential transfer to a pal-
liative setting, and fear of recurrence. Fear of recurrence is
one of the most commonly reported issues and an import-
ant area of unmet need for cancer survivors [24]. A lack of
outward physical symptoms in ovarian cancer also means
that self-monitoring is difficult [23]. In the same study of
ovarian cancer patients, depression was highest before treat-
ment (25\%) and during treatment (23\%), and reduced fol-
lowing treatment (13\%). This is in the context of a lifetime
prevalence for clinical depression and anxiety of around 10
and 8\%, respectively, amongst women in the UK [23, 25].
A similar systematic review of depression and anxiety
among patients with prostate cancer found that anxiety
tended to be highest pre-treatment (27\%) and lowered
during treatment (15\%) and post-treatment (18\%) [26].
Rates of depression were relatively similar following
treatment (18\%), during treatment (15\%) and pre-
treatment (17\%), with the 95\% confidence intervals for
these estimates largely overlapping. For reference, the
prevalence of clinical depression and anxiety in men
aged over 65 years is less than 9 and 6\%, respectively
[26]. A systematic review on the prevalence of psycho-
logical distress among testicular cancer survivors dem-
onstrated that around one in five experienced clinically
significant anxiety, compared to one in eight among
general population controls, with fear of recurrence
again being one of the key issues reported [27]. How-
ever, depression was no more prevalent amongst those
surviving testicular cancer compared to the general
population. In Scotland, the prevalence of depression
was found to be highest in patients with lung cancer
(13\%), followed by gynaecological cancer (11\%), breast
cancer (9\%), colorectal cancer (7\%), and genitourinary
cancer (6\%) [28]. The authors found depression to be
more likely among younger and more socially disadvan-
taged individuals. In addition, 73\% of the patients with
depression were not receiving treatment for their men-
tal health. Further research is needed to ascertain the
factors which contribute to the uptake and efficacy of
treatment for depression. This study also only consid-
ered people with cancer who had attended specialist
cancer clinics within a defined time period, which likely
excluded people who were diagnosed many years ago.
The longer-term psychological impact of cancer has
received comparatively little research. The few studies in
this area have mainly focused on women with breast
cancer and demonstrate that depressive symptoms can
persist for over 5 years after diagnosis, though the preva-
lence of anxiety was not elevated compared to the
general population [29]. A systematic review of the
prevalence of depression and anxiety among long-term
cancer survivors, including all types, found that anxiety
was more prevalent among cancer survivors, compared
to healthy controls [30]. Few studies have focused specif-
ically on younger cancer survivors and more research is
needed in this area. A representative study of young
adult cancer survivors aged 15 to 39 years in the United
States demonstrated that moderate (23\% vs 17\%) and se-
vere (8\% vs 3\%) mental distress were significantly higher
in those living with cancer for at least 5 years after diag-
nosis, compared to controls [31]. 75 and 52\% of people
with cancer with moderate and severe distress, respect-
ively, had not talked to a mental health professional,
with the cost of treatment a potential barrier. Limita-
tions of this study included the focus on self-reported
mental distress and not clinical depression or anxiety, as
well as the relatively small sample size.
Many studies in this area have a poor response rate,
lack representativeness, are based on a small sample of
patients (often with the most common types of cancer),
which often exclude those with cognitive impairment
and patients who are too physically or mentally unwell
to take part [32]. Future studies would benefit from
using administrative health data [33], for example, link-
ing together cancer registries, inpatient and outpatient
records and prescribing data. There are also a lack of
studies covering populations from low- and middle-
income countries [34]. The estimated prevalence of co-
morbid common mental disorders is likely to vary de-
pending on the country studied, due to factors such as
the health and welfare system. These factors may influ-
ence mental health inequalities among people with can-
cer, which has received little research focus. In a
Scottish study, depression was found to be higher in the
least advantaged groups (19\%), compared to the most
advantaged (10\%) [35]. Cancer and comorbid anxiety
was also unequally distributed; in the least advantaged
groups around 12\% had both conditions, compared to
7\% among the most advantaged [35]. Further research is
needed in this area to quantify, monitor and prevent in-
equalities among people with cancer.
It should also be highlighted that the psychological im-
pact of cancer may not always be negative and many
people will not experience problems with depression and
anxiety. Experiencing temporary distress related to a can-
cer diagnosis may lead to positive psychological changes
in the long-term whereby individuals feel a greater appre-
ciation of life and are able to re-evaluate their priorities
[36]. The factors that protect against the development of
common mental disorders and contribute to positive
Niedzwiedz et al. BMC Cancer (2019) 19:943 Page 4 of 8
mental health among people living with and beyond can-
cer merits further research.
Treatment and management of depression and anxiety
among people with cancer
To effectively manage and treat depression and anxiety
among people with cancer, symptoms must first be identi-
fied. However, several social and clinical barriers have
been reported. A key issue is the lack of physician time for
assessing symptoms. There can also be a normalisation of
distress and attribution of the somatic symptoms of
depression and anxiety to the cancer. Patients may not
disclose psychiatric symptoms because of the stigma sur-
rounding mental health conditions [37]. Screening for de-
pression and anxiety among patients with cancer is also
only of value if it leads to effective treatment and support
that is able to improve patient outcomes. Patients may be
more reluctant to discuss their mental health needs if they
perceive a lack of effective treatment options.
The existing evidence for treating anxiety and depression
among patients with cancer is limited and of varying quality
[38]. Studies with small sample sizes are common; this miti-
gates against the detection of meaningful changes in patient
outcomes and these studies often suffer from a high rate of
attrition, which likely reflects the high symptom burden
and reduced survival in this patient population [39]. Sys-
tematic reviews demonstrate there is a preponderance of
studies from the United States, which include a high num-
ber of studies focusing on female patients with breast
cancer [40]. However, these studies demonstrate that psy-
chotherapy, psychoeducation and relaxation training may
have small to medium short-term effects on relieving emo-
tional distress and reducing symptoms of anxiety and
depression, as well as improving health-related quality of
life. The evidence for pharmacological treatment of depres-
sion with antidepressants is mixed - there are very few
studies in this area and those that exist are of low quality
[41]. There is also concern around potential side effects of
antidepressants and drug interactions that may affect the
efficacy of cancer treatments [42].
A systematic review and meta-analysis focusing on
cognitive behavioural therapy (CBT) found that it may
be effective in reducing depression and anxiety and im-
proving quality of life in patients with cancer in the
short-term, but potential long-term effects were only
sustained for quality of life [43]. However, in this meta-
analysis the included participants were primarily women
with breast cancer and there are a lack of studies cover-
ing other cancer types. It is likely that collaborative care
interventions which involve partnership between psych-
iatry, clinical psychology and primary care, overseen by a
care manager are likely to be most effective in the man-
agement and treatment of depression amongst people
with cancer [44]. Treatment should be based on patient
preference and also take into account potential adverse
side effects [44]. In a UK-based study it was found that
only a third of patients with cancer and related psycho-
logical or emotional distress were willing to be referred
for support [45]. Qualitative studies also demonstrate
that patients often do not want to discuss their feelings
with nurses during cancer treatment [46]. However, pa-
tients valued having the option to talk about their emo-
tions, but they preferred to choose with whom and
when. There is therefore a need for further research into
some of the barriers to obtaining mental health support
among those affected by cancer and experiencing dis-
tress to prevent future problems.
The self-management of psychological distress among
people with cancer may be beneficial and could help pre-
vent distress becoming clinical depression or anxiety. Self-
management can be defined as: “The individual’s ability to
manage the symptoms, treatment, physical and psychosocial
consequences and lifestyle changes inherent in living with a
chronic condition. Efficacious self-management encompasses
the ability to monitor one’s condition and to affect the cogni-
tive, behavioural and emotional responses necessary to
maintain a satisfactory quality of life. Thus, a dynamic and
continuous process of self-regulation is established.” [47].
Studies on self-management, cancer and psychological dis-
tress have focused on the treatment phase, with fewer
investigating interventions following treatment or at the
end of life [48]. There is evidence to suggest that self-
management of psychological distress in cancer can help to
empower patients and families to care for themselves in a
way which is preferable for them. Self-management inter-
ventions that have shown promise include education, moni-
toring, teaching and counselling to help patients manage
the short- and long-term physical and psychosocial effects
of cancer [48]. However, a recent systematic review exam-
ining the impact of self-management interventions on
outcomes including quality of life, self-efficacy and symp-
tom management (such as psychological distress) amongst
cancer survivors demonstrated a lack of evidence to sup-
port any specific intervention and found that the six
included interventions lacked sustainability, bringing into
question their long-term effectiveness and value for money
[49]. Again, the included studies were dominated by
women with breast cancer, with only two covering other
cancers.
Effective treatment and management strategies may
also differ according to the demographic group affected.
In a report by CLIC (Cancer and Leukaemia in Child-
hood) Sargent which surveyed 146 young people with
cancer, keeping in touch with friends and family, talking
to others with similar experiences and access to the
internet in hospital were reported to help maintain men-
tal health during cancer treatment [50]. Of the young
people who mentioned they would find it helpful to talk
Niedzwiedz et al. BMC Cancer (2019) 19:943 Page 5 of 8
to other people with similar experiences, 60\% said they
would prefer to do this online. Young people also
reported that the available services were not tailored to
deal with those aged under 18 or the emotional impact
of cancer. In addition, those who accessed services men-
tioned that there is a lack of suitable long-term emo-
tional support. Just over 40\% of the young people who
took part did not access support for their mental health
needs.
It is clear that a more personalised approach to support-
ing the psychological health of people with cancer is
needed [51]. Some people may not want or require sup-
port or treatment, others will be able to self-manage, and
some may have more complex needs that require more
intensive follow-up and support. At diagnosis, the psycho-
logical health of patients should be considered alongside
their physical health and sources of support offered. Needs
and symptoms may also change over time. Evaluation of
more recent personalised approaches to follow-up care
that have been adopted in several areas including England
and Northern Ireland [51] are needed to understand the
role they may have in preventing longer term depression
and anxiety amongst cancer survivors.
A key barrier affecting research progress in this area is
funding [52]. In the UK, money spent on research into
the biology of cancer was more than five times than that
spent on ‘Cancer Control, Survivorship and Outcomes’
during 2017/18 [53]. Research into the mental health
and wellbeing of people living with and beyond cancer
is likely to only be a small part of this. Research is ur-
gently needed in this area as more people survive cancer
and for some cancers, such as multiple myeloma and
colorectal cancer, risk is increasing in younger cohorts
[54]. The long-term (those that begin during treatment
and continue afterwards) and late effects of cancer treat-
ment (those that begin after treatment is completed),
such as secondary cancers, infertility, chronic pain and
insomnia, are likely to affect the mental wellbeing of
cancer survivors, potentially contributing to depression
and anxiety [6]. The National Cancer Research Institute
(NCRI) in the UK have also recently highlighted re-
search into the short-term and long-term psychological
impacts of cancer and its treatment as a key priority, fol-
lowing surveys of over 3500 patients, carers, and health
and social care professionals [55].
Conclusion
The mental health of people living with and beyond cancer
in its various types and stages is an important and growing
research and clinical priority. Compared to the general
population, the prevalence of anxiety and depression is often
higher among people with cancer, but estimates vary due to
a number of factors, such as the type and stage of cancer.
Patients often do not obtain psychological support or
treatment. This is likely due to several factors, including lack
of awareness and identification of psychiatric symptoms, an
absence of support available or offered, lack of evidence
around effective treatments, stigma, and patient preference.
In particular, we highlight the lack of high-quality research
into the mental health of long-term cancer survivors, the po-
tential impact of long-term and late effects of cancer treat-
ment, and the few studies focused on prevention. Further
research that includes the less common types of cancer is
required, as well as the inclusion of younger people and
populations from low- and middle-income countries. Given
the increasing numbers of people living with and beyond
cancer, this research is of timely importance.
Abbreviations
CBT: Cognitive behavioural therapy; CESD: Center for Epidemiologic Studies
Depression Scale; CINAHL: Cumulative Index to Nursing and Allied Health
Literature; CLIC: Cancer and Leukaemia in Childhood; HADS-D: Hospital
Anxiety and Depression Scale - depression subscale; NCRI: National Cancer
Research Institute
Acknowledgements
This article is built on a literature review conducted by CLN and LK for a
project on ‘Supporting the mental and emotional health of people with
cancer’ funded by the Big Lottery Fund when CLN was an employee of the
Mental Health Foundation in Scotland during 2017.
Authors’ contributions
CLN and LK conceived the article. CLN conducted the searches and drafted
the manuscript. CLN, LK, SVK, KAR and DJS interpreted the findings. All
authors critically revised the manuscript, read and approved the final version.
Funding
CLN is currently supported by the Medical Research Council (grant number
MR/R024774/1). SVK is funded by a NHS Research Scotland (NRS) Senior
Clinical Fellowship (SCAF/15/02), the Medical Research Council
(MC_UU_12017/13 & MC_UU_12017/15) and Scottish Government Chief
Scientist Office (SPHSU13 & SPHSU15). The funders had no role in the study
design; collection, analysis and interpretation of data; the writing of the
article; and in the decision to submit it for publication.
Availability of data and materials
All data generated or analysed during this study are included in this
published article.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests except for the
funding acknowledged.
Author details
1Institute of Health & Wellbeing, University of Glasgow, Glasgow, Scotland,
UK. 2University of Strathclyde, Centre for Health Policy, Glasgow, Scotland,
UK. 3Mental …
Are We Accurately Evaluating Depression in Patients With Cancer?
Rebecca M. Saracino
Memorial Sloan Kettering Cancer Center, New York, New
York, and Fordham University
Ezgi Aytürk and Heining Cham
Fordham University
Barry Rosenfeld
Memorial Sloan Kettering Cancer Center, New York, New
York, and Fordham University
Leah M. Feuerstahler
Fordham University
Christian J. Nelson
Memorial Sloan Kettering Cancer Center, New York, New York
Depression remains poorly managed in oncology, in part because of the difficulty of reliably screening
and assessing for depression in the context of medical illness. Whether somatic items really skew the
ability to identify “true” depression, or represent meaningful indicators of depression, remains to be
determined. This study utilized item response theory (IRT) to compare the performance of traditional
depression criteria with Endicott’s substitutive criteria (ESC; tearfulness or depressed appearance; social
withdrawal; brooding; cannot be cheered up). The Patient Health Questionnaire (PHQ-9), ESC, and
Center for Epidemiologic Studies Depression Scale (CES-D) were administered to 558 outpatients with
cancer. IRT models were utilized to evaluate global and item fit for traditional PHQ-9 items compared
with a modified version replacing the 4 somatic items with ESC. The modified PHQ-9 ESC scale was
the best fit using a partial credit model; model fit was improved after collapsing the middle 2 response
categories and removing psychomotor agitation/retardation. This improved model showed satisfactory
scale precision and internal consistency, and was free from differential item functioning for gender, age,
and race. Concurrent and criterion validity were supported. Thus, as many have speculated, utilizing the
ESC may result in more accurate identification of depressive symptoms in oncology. Depressed mood,
anhedonia, and suicidal ideation retained their expected properties in the modified scale, indicating that
the traditional underlying syndrome of depression likely remains the same, but the ESC may provide
more specificity when assessing patients with cancer.
Public Significance Statement
Alternative approaches to assessing depression in patients with cancer may be more accurate than
current approaches, which rely heavily on physical symptoms. An improved approach might
eliminate physical symptoms and focus more on emotional symptoms.
Keywords: depression, diagnostic criteria, oncology, IRT, screening
Supplemental materials: http://dx.doi.org/10.1037/pas0000765.supp
Accurate assessment of depression in patients with medical illness
is critically important, as those with comorbid mood disorders are at
significantly greater risk for nonadherence to medical treatments and
premature mortality (DiMatteo, Lepper, & Croghan, 2000; Misono,
Weiss, Fann, Redman, & Yueh, 2008). Historically, clinicians and
researchers have debated whether or not the reliance on somatic
This article was published Online First August 8, 2019.
X Rebecca M. Saracino, Department of Psychiatry and Behavioral
Sciences, Memorial Sloan Kettering Cancer Center, New York, New York,
and Department of Psychology, Fordham University; Ezgi Aytürk and
Heining Cham, Department of Psychology, Fordham University; Barry
Rosenfeld, Department of Psychiatry and Behavioral Sciences, Memorial
Sloan Kettering Cancer Center, and Department of Psychology, Fordham
University; X Leah M. Feuerstahler, Department of Psychology, Fordham
University; Christian J. Nelson, Department of Psychiatry and Behavioral
Sciences, Memorial Sloan Kettering Cancer Center.
This research was supported by funding from the National Institutes of
Health (T32CA009461 and P30CA008748).
Correspondence concerning this article should be addressed to Rebecca
M. Saracino, Department of Psychiatry and Behavioral Sciences, Memorial
Sloan Kettering Cancer Center, 641 Lexington Avenue, 7th Floor, New
York, NY 10022. E-mail: [email protected]
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
Psychological Assessment
© 2019 American Psychological Association 2020, Vol. 32, No. 1, 98 –107
ISSN: 1040-3590 http://dx.doi.org/10.1037/pas0000765
98
items when rendering a depression diagnosis inappropriately in-
flates the prevalence of depressive disorders among the medically
ill, especially in oncology settings (Jones et al., 2015; Krebber et
al., 2014; Saracino, Rosenfeld, & Nelson, 2018). Somatic items
(i.e., sleep disturbance, fatigue, appetite changes, diminished con-
centration) may reflect side effects of treatment or the pathology of
the underlying illness itself. Despite this concern, the Patient
Health Questionnaire-9 item (PHQ-9; Kroenke & Spitzer, 2002),
which relies exclusively on Diagnostic and Statistical Manual of
Mental Disorders criteria, remains one of the most widely utilized
depression screening measures across medical settings (e.g., pri-
mary care, oncology, cardiovascular disease; Dyer, Williams,
Bombardier, Vannoy, & Fann, 2016; Forkmann, Gauggel, Span-
genberg, Brähler, & Glaesmer, 2013; Gothwal, Bagga, & Suma-
lini, 2014; Kendel et al., 2010; Pedersen, Mathiasen, Christensen,
& Makransky, 2016; Williams et al., 2009).
The PHQ-9 consists of nine items, each of which corresponds to
one of the nine symptoms required for a diagnosis of a major
depressive disorder (MDD) as defined by the Diagnostic and
Statistical Manual of Mental Disorders (DSM; American Psychi-
atric Association, 2013). Respondents are asked to rate how often
they have been bothered by each of the nine symptoms over the
preceding 2 weeks. Respondents rate each item on a 4-point scale
(0 � not at all, 1 � several days, 2 � more than half the days, 3 �
nearly every day). Due to its popularity, a handful of studies have
used item response theory (IRT) to examine the PHQ-9 in samples
of medical patients (Dyer et al., 2016; Forkmann et al., 2013;
Gothwal et al., 2014; Kendel et al., 2010; Pedersen et al., 2016;
Williams et al., 2009). For example, Kendel et al. (2010) observed
that among 1,271 patients undergoing coronary artery bypass graft
surgery, most of the somatic items on the PHQ-9 did not meet
criteria for a good overall model fit (i.e., according to fit statistics).
Instead, they found that six out of seven items on the Hospital
Anxiety and Depression Scale Depression subscale (HADS-D;
Zigmond & Snaith, 1983), which rely entirely on cognitive and
affective symptoms, and the two PHQ-9 items reflecting the DSM
gateway symptoms of MDD (i.e., depressed mood and anhedonia)
plus fatigue, were the strongest indicators of the underlying con-
struct. They also identified differential item functioning (DIF)
across genders on two PHQ-9 items; women were more likely than
men to endorse depressed mood and fatigue conditional on the
latent trait. In theory, DIF is an undesirable property of an item, as
it indicates that respondents from different groups (e.g., males and
females) with the same level of the latent trait have different
probabilities of endorsing an item (Holland & Wainer, 1993).
A study of 1,531 patients with heart disease and implantable
cardioverter defibrillators identified PHQ-9 items reflecting de-
pressed mood, feeling bad about yourself or that you are a failure,
and suicidal ideation, as being the best items for discriminating
individuals with higher and lower levels of depression (Pedersen et
al., 2016). They also found significant DIF for gender for the
depressed mood item, such that women were more likely than men
to endorse this item at the same underlying level of depression.
Additionally, overall model fit was substantially improved after
collapsing the two middle response options (several days and more
than half the days) in the 4-point scale, indicating that these two
response options were not meaningfully distinguished from one
another. Another study of 100 adults with a history of traumatic
brain injury demonstrated similar findings, as all PHQ-9 items
demonstrated good fit when the two intermediate response cate-
gories were collapsed (Dyer et al., 2016). Thus, regardless of the
relative performance of individual items across clinical samples, a
collapsed, three response option format may be most suitable for
the PHQ-9.
In oncology settings, alternative approaches to depression as-
sessment have been proposed (e.g., Cavanaugh, 1995; Endicott,
1984) in order to increase the specificity of depression screening
measures and decrease the potential overinclusivity of the criteria
used by the DSM. The most widely recognized of these approaches
are the substitutive criteria proposed by Endicott (1984; ESC), who
recommended replacing the four somatic symptoms with four
alternative symptoms: tearfulness or depressed appearance in face
or body posture; social withdrawal or decreased talkativeness;
brooding, self-pity, or pessimism; and cannot be cheered up,
doesn’t smile, no response to good news or funny situations.
Although widely cited, there is a dearth of published research that
has systematically evaluated this proposal.
Only one prior study has utilized IRT to compare the perfor-
mance of traditional DSM criteria with the Endicott substitutive
approach, using a structured clinical interview to rate each of
the criteria under investigation. Akechi et al. (2009) examined
the utility of the DSM–IV criteria for MDD, along with the
Endicott’s substitutive criteria and those proposed by Ca-
vanaugh (1995), who recommended replacing the four DSM
somatic items with two behavioral criteria: “not participating in
medical treatment in spite of ability to do so” and “functioning
at a lower level than medical condition warrants or failure to
progress in recovery despite improved medical condition.” In a
sample of 728 cancer patients diagnosed with depression (based
on DSM–IV criteria), these authors found that the Endicott and
Cavanaugh’s criteria were among the symptoms with the most
utility in assessing depression across the spectrum of severity.
Endicott’s “tearfulness or depressed appearance” and “brood-
ing, self-pity, or pessimism” were particularly good indicators
of mild depression, while “not participating in medical care”
(Cavanaugh) and “social withdrawal” (Endicott) were good
indicators of moderate to severe depression. For patients with
severe depression, Endicott’s “cannot be cheered up . . .”
symptom was the most salient indicator. Although none of the
DSM–IV criteria had a high ability to discriminate between
individuals with more or less severe depression in this sample,
this finding may have been impacted by their study methodol-
ogy, because they included only patients that met DSM criteria
for MDD (thereby reducing the variability in the DSM–IV
symptoms). Nevertheless, the authors suggested that the sub-
stitutive criteria proposed by Endicott and Cavanaugh are prom-
ising, given their apparent utility in discriminating depressive
symptom severity. In addition to a restricted symptom range
due to inclusion criteria, this study also relied on clinician
interview, which is a costly and unrealistic approach to depres-
sion screening, particularly in busy oncology settings in which
clinicians do not have the training nor the time to conduct
psychiatric diagnostic interviews.
Despite its popularity, no studies to date have utilized IRT to
examine the PHQ-9 in patients with cancer, nor have these
methods been extended to study the Endicott’s substitutive
criteria in a self-report format. Cancer and its treatment have
unique disease sequelae and treatment side effects that are not
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
99DEPRESSION EVALUATION IN CANCER
necessarily as salient in other medical conditions such as heart
disease or brain injury. While fatigue may be cross-cutting,
symptoms such as appetite, concentration, and sleep distur-
bances are particularly salient in oncology (Akechi et al., 2003).
Given its wide popularity and development for specific use in
oncology, the present study focused on the classic symptoms of
MDD and Endicott’s criteria only; the alternative symptoms
proposed by Cavanaugh were not included in the current study
as they were developed for general medical settings, not spe-
cifically for use with cancer patients. While depression screen-
ing measures can identify general distress, dysphoria, and sub-
syndromal depression (in addition to MDD), the goal of the
current study was to evaluate the DSM criteria for MDD (via the
PHQ-9) and the Endicott’s substitutive criteria as a first step
toward further psychometric validation of the substitutive ap-
proach. The present study searched for the best-fitting measure-
ment structure for the 13 items (nine DSM criteria plus four
Endicott’s substitutive criteria items) using several IRT models.
Differential item functioning (DIF) of the selected measurement
structure was also tested across gender (males vs. females), age
(40 – 69 years old vs. 70 or above), and racial groups (non-
Hispanic White participants vs. ethnic minority participants), as
well as precision and internal consistency of scale scores and
concurrent validity of score interpretations.
Method
Participants and Procedure
Participants were recruited from outpatient clinics at Memorial
Sloan Kettering Cancer Center (MSK) between January 2016 and
May 2016. To be eligible for participation, patients had to be 40
years or older,1 fluent in English, and have a cancer diagnosis.
Patients were approached by trained research personnel while
awaiting routine clinic appointments; those who were eligible were
informed of the study procedures, risks, and benefits, and invited
to participate. The study was approved by the MSK and Fordham
University Institutional Review Boards.
Measures
All participants completed a packet of questionnaires in a fixed
order, including the Patient Health Questionnaire-9 (PHQ-9) and
four items assessing the Endicott criteria. Table 1 presents the
PHQ-9 items and Endicott’s substitutive criteria (ESC) items,
along with the percentage endorsing each response option. As
noted above, respondents were asked to rate how often they have
been bothered by the symptoms described by the items over the
last 2 weeks on a 4-point scale (0 � not at all, 1 � several days,
2 � more than half the days, 3 � nearly every day). Endicott
(1984) proposed four alternative symptoms (tearfulness or de-
pressed appearance in face or body posture; social withdrawal or
decreased talkativeness; brooding, self-pity, or pessimism; and
cannot be cheered up, doesn’t smile, no response to good news or
funny situations) as substitutes for four DSM symptoms that are
most commonly confounded by medical illness (sleep disturbance,
fatigue, appetite changes, diminished concentration). These four
items were assessed using the same instructions and response scale
as PHQ-9 items.
Participants were also administered the Center for Epidemio-
logic Studies Depression Scale (CES-D; Radloff, 1977), a self-
report measure of 20 depressive symptoms. Past research indicates
acceptable psychometric properties and has supported a four-factor
structure: depressed affect, positive affect, somatic complaints, and
interpersonal problems (Nelson, Cho, Berk, Holland, & Roth,
2010; Saracino, Cham, Rosenfeld, & Nelson, 2018; Vodermaier,
Linden, & Siu, 2009). The CES-D was used to examine the
concurrent validity of PHQ-9 and ESC item scores; it was not
included in IRT analyses as the primary focus was on approximat-
ing DSM criteria for MDD, which are more directly assessed by
the PHQ-9. Sociodemographic and medical data were also col-
lected by participant self-report.
Data Analyses
Missing data analysis. A total of 663 patients completed the
study questionnaires. Missing data rates for the PHQ-9 and ESC
items were low (M � 7.2\%, range: 6.5\% to 7.7\%). The differences
between the sample with complete data (N � 558) and those with
missing observations were small in effect sizes (all Cohen’s d �
.29 and W � .15; Cohen, 1988) across sociodemographic and
medical data, indicating that listwise deletion was appropriate to
handle cases with the missing values.
IRT analysis. Following prior studies (e.g., Dyer et al., 2016;
Forkmann et al., 2013; Gothwal et al., 2014; Kendel et al., 2010;
Lamoureux et al., 2009; Pedersen et al., 2016; Williams et al.,
2009), two polytomous Rasch models were used: the partial credit
model (PCM; Masters, 1982) and the rating scale model (RSM;
Andrich, 1978). Two polytomous non-Rasch models were also
analyzed: the generalized partial credit model (GPCM; Muraki,
1992) and graded response model (GRM; Samejima, 1969). Rasch
models (PCM and RSM) use observed item response patterns to
estimate a person’s ability (in this case, depression severity) and an
item’s difficulty (depression level that the item represents) on a
continuous latent variable (depression). It models the probability
of a given response as a logistic function of the difference between
a person’s ability and item difficulty (Andrich, 1978). With di-
chotomous data (e.g., yes/no or correct/incorrect), the higher the
person’s ability relative to the item difficulty, the more likely a
person is to endorse the item. With polytomous data, Rasch models
estimate the response category threshold parameters. Category
thresholds refer to the point where the probability of choosing
either one of two adjacent response options (e.g., “not at all” vs.
“several days”) is equal. RSM is the simplest (most constrained)
polytomous Rasch model which assumes equal category thresh-
olds across all items of a given scale and estimates a difficulty
parameter for each item. The PCM is more relaxed than RSM as
it estimates separate item thresholds for each item. However,
both models assume the same discrimination for all items (i.e.,
the degree to which an item differentiates people with different
depression levels). In these two models, average or sum scores
1 Age 40 was selected as the inclusion criteria cut-off in order to
differentiate the sample from what the National Comprehensive Cancer
Network (Coccia et al., 2018) operationalized as “adolescent and young
adult,” which refers to patients from 15 to 39 years of age. This age group
was selected as the primary purpose was to examine depression assessment
in adults.
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
100 SARACINO ET AL.
of the items can be used as the overall scale score. The GPCM
and GRM differ from the polytomous Rasch models in that they
estimate different discrimination parameters for each item (the
degree to which an item differentiates people with different
depression levels). Because the items can have different dis-
criminating power in GPCM and GRM, both models require
specialized algorithms to computing the scale scores. Unlike the
GPCM, the GRM estimates the probability of choosing a par-
ticular response category or above, but assumes that the item
category thresholds are always ordered.
Three indices of model fit criteria were used to select the
best-fitting model(s): (a) C2 goodness-of-fit test statistic (Cai &
Monroe, 2014; Maydeu-Olivares & Joe, 2006); (b) Akaike Infor-
mation Criterion (AIC; small value indicates better model fit); and
(c) Bayesian Information Criterion (BIC; small value indicates
better model fit). The unidimensional structure was first tested
with PHQ-9 items only (termed PHQ-9-Original) and then a uni-
dimensional structure with the four PHQ-9 items (sleep distur-
bances, fatigue, appetite changes, trouble concentrating) substi-
tuted by the ESC items (termed PHQ-9-Substitutive). Both
measurement structures were tested with the PCM, RSM, GPCM,
and GRM models. Based on the results of these analyses, the
models were modified by collapsing the response options of the
items and removing items that negatively impacted model fit
(described in more detail below).
DIF analysis. After deciding on the optimal measurement
structure for the IRT analysis, the simultaneous item bias test
(SIBTEST; Shealy & Stout, 1993) was used to examine if there
was differential functioning of PHQ-9 and Endicott items across
gender (males: n � 288 vs. females: n � 270), age (younger:
40 – 69 years old; n � 380 vs. older: 70 or above; n � 178), and
racial groups (non-Hispanic White: n � 455 vs. ethnic minority
participants: n � 103). Age 70 was used to bifurcate the sample as
patients with cancer who are over 70-years-old have been shown to
experience significantly more medical comorbidity that those
younger than 70 (Bluethmann, Mariotto, & Rowland, 2016). Both
uniform DIF and nonuniform DIF were tested with one crossing
point (Chalmers, 2018; Li & Stout, 1996). The SIBTEST estimates
a standardized mean difference (�) capturing the group differences
in correct response probabilities (� � 0 indicates no DIF) and
provides a significance test to determine if � is significantly
different from zero. � values between zero and .05 are considered
small DIF, between .05 and .1 are considered moderate DIF, and
.1 or above are considered large DIF (Shealy & Stout, 1993). To
avoid inflated Type I error rate due to multiple testing of � for each
item, Holm’s (1979) procedure was used to adjust p values (Kim
& Oshima, 2013).
Validity analysis. The proportion of participants who ob-
tained the lowest possible scale score on the PHQ-9-Original and
on the selected substitutive measurement structure was calculated.
It was expected that there would be a higher proportion of patients
with a scale score of zero in the selected substitutive structure. To
examine the convergent and discriminant validity of the selected
substitutive structure and compare the relative differences between
the selected substitutive structure and PHQ-9-Original, we calcu-
lated the correlations between the scale scores of the selected
substitutive structure, PHQ-9-Original, and the CES-D total score
and factors (depressed affect, positive affect, somatic complaints,
and interpersonal problems). It was expected that there would be
larger correlations between the selected substitutive structure and
the CES-D depressed affect factor and total scores, because the
depressed affect factor is most closely aligned with the affective
DSM criteria. Finally, participants who reported receiving treat-
ment for depression and those who did not were compared on the
scale scores of the selected substitutive structure and PHQ-9-
Original. It was anticipated that the difference between the two
groups would be larger on the selected substitutive structure than
the PHQ-9-Original.
All IRT and DIF analyses (except for person separation reliabil-
ity; described in more detail below) were conducted using the R
mirt package (Version 1.29; Chalmers, 2012). Person separation
reliability indices were calculated using the R eRm package (Ver-
sion 0.16 –1; Mair & Hatzinger, 2007).
Table 1
Percentage (\%) of Response Options of PHQ-9 and Endicott’s Substitutive Criteria Items
Abbreviated item label
Percentage (\%) endorsing response option
Not at all
(0)
Several days
(1)
More than half
the days (2)
Nearly every
day (3)
Patient Health Questionnaire-9 Item (PHQ-9)
1. Anhedonia 64.0 21.5 9.1 5.4
2. Depressed mood 63.8 26.0 6.4 3.8
3. Sleep disturbances 44.8 28.7 14.0 12.5
4. Fatigue 32.4 37.5 15.4 14.7
5. Appetite changes 58.6 22.6 10.2 8.6
6. Feeling bad about yourself 77.8 14.9 4.8 2.5
7. Trouble concentrating 65.1 23.7 6.4 4.8
8. Psychomotor agitation and retardation 80.3 10.9 5.9 2.9
9. Suicidal ideation 92.6 6.3 .9 .2
Endicott’s substitutive criteria
1. Socially withdrawn 75.1 16.5 5.0 3.4
2. Tearfulness 78.1 14.7 5.0 2.2
3. Brooding 71.1 21.0 4.7 3.2
4. Could not be cheered up 82.8 12.2 3.9 1.1
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
101DEPRESSION EVALUATION IN CANCER
Results
Participant Characteristics
The sample (N � 558) was approximately evenly split by
gender (51.6\% male; n � 288) and ranged in age from 40 to 90
years or older2 (M � 64.7, SD � 10.3; see Table 2). Most
participants were White (87.6\%; n � 489; including n � 455
non-Hispanic and n � 34 Hispanic), married or living with a
partner (70.6\%; n � 394), and had a college and/or graduate
education (70.4\%; n � 393). The most common cancer diagnoses
were gynecological (16.8\%; n � 94), lung (15.2\%; n � 85), and
prostate (13.1\%; n � 73). Over one third of participants reported
Stage IV disease (37.5\%; n � 209). The majority of participants
had received active cancer treatment within the preceding 6
months (71.3\%; n � 398).
Initial Analysis of Unidimensionality
Confirmatory factor analysis (CFA) was conducted to test the
unidimensionality of the PHQ-9-Original and PHQ-9-Substitutive.
Models were estimated using polychoric correlations and diago-
nally weighted least squares estimation via the R lavaan package
(Rosseel, 2012). A full report of the results can be found in the
online supplementary materials. The comparative fit index (CFI)
and Tucker-Lewis index (TLI) suggested good model fit of both
the PHQ-9-Original and PHQ-9-Substitutive (all values � .99);
however, the PHQ-9-Original had slightly worse RMSEA than
PHQ-9-Substitutive (i.e., .066 vs. .028, respectively). Taken to-
gether, these model fit indices suggest that both PHQ-9-Original
and PHQ-9-Substitutive were sufficiently unidimensional for IRT
analysis.
IRT Analysis
All the IRT models (PCM, RSM, GPCM, GRM) converged
properly in the PHQ-9-Original and PHQ-9-Substitutive measure-
ment structures. Panels A and B in Table 3 present the global
model fit results for the IRT models of the two structures. Com-
pared with PHQ-9-Original, the PHQ-9-Substitutive structure had
a better model fit in terms of AIC and BIC across all four IRT
models. Therefore, the remaining analyses used only the PHQ-9-
Substitutive structure. However, the PHQ-9-Subsitutive structure
generated a significant C2 test statistic (ps � .001) for all four
models, indicating that none of the models fit the data well. Since
the more complex GPCM and GRM did not fit better than PCM
and RSM, they were not considered further.3
Next, the item fit of the PCM and RSM were compared using
the PHQ-9-Substitutive structure using: (a) S-�2 item fit test sta-
tistic (Kang & Chen, 2008; Orlando & Thissen, 2000); and (b)
item infit (information weighted mean square), where a value of
1.0 indicates perfect fit and values between 0.7 and 1.3 are con-
sidered acceptable fit (Wright & Linacre, 1994). Results showed
that Item 8 on the PHQ-9 (“moving or speaking so slowly that
other people could have noticed or the opposite— being so fidgety
or restless that you have been moving a lot more than usual”) was
the only item that showed both significant S-�2 test statistics,
PCM: S-�2(df � 22) � 39.63, p � .01; RSM: S-�2(df � 23) �
45.37, p � .004, as well as infit values beyond the acceptable
range (PCM: 1.45; RSM: 1.57). Following Forkmann et al. (2013)
and Kendel et al. (2010), this item was removed from PHQ-9-
Substitutive structure and the PCM and RSM models were fit
again to this new structure (termed PHQ-8-Substitutive).4
In PHQ-8-Substitutive, the PCM had a significant C2 test sta-
tistic, C2(df � 27) � 46.7, p � .01, while RSM did not, C2(df �
2 Due to HIPPA protection participants who were 90 years or older (n �
2) checked a box indicating they were in this age range.
3 GPCM and GRM results for all steps are available upon request.
4 Analysis of the PCM threshold parameter estimates and item response
curves of this item in PHQ-9-Substitutive supported this decision (avail-
able upon request).
Table 2
Demographic Characteristics
Demographic Frequency \%
Gender
Male 288 48.4
Female 270 51.6
Race
White 489 87.6
African American 29 5.2
Asian or Pacific Islander 21 3.8
Other 19 3.4
Ethnicity
Hispanic 48 8.6
Not Hispanic 510 91.4
Marital status
Single (never married) 40 7.2
Married/living with partner 394 70.6
Divorced/separated 75 13.4
Widowed 49 8.8
Education
Did not …
ORIGINAL ARTICLE
Hospital Anxiety and Depression Scale (HADS) accuracy
in cancer patients
Maria Antonietta Annunziata1 & Barbara Muzzatti1 & Ettore Bidoli2 & Cristiana Flaiban1 & Francesca Bomben1 &
Marika Piccinin1 & Katiuscia Maria Gipponi1 & Giulia Mariutti1 & Sara Busato1 & Sara Mella1
Received: 5 April 2019 /Accepted: 11 December 2019
# Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract
Purpose The Hospital Anxiety and Depression Scale (HADS) is a self-report questionnaire designed to screen anxious and
depressive states in patients in non-psychiatric settings. In spite of its large use, no agreement exists in literature on HADS
accuracy in case finding. The present research addresses the issue of HADS accuracy in cancer patients, comparing its two
subscales (HADS-A and HADS-D) against tools not in use in psychiatry, which are able to detect prolonged negative emotional
states.
Methods 2121 consecutive adult cancer inpatients were administered the HADS together with the State Anxiety subscale of
State-Trait Anxiety Inventory and the Center for Epidemiologic Studies Scale on Depression. Receiver operating characteristic
(ROC) curves were computed to identify a cut-off for anxious and depressive states in cancer patients. All indicators were
computed together with their corresponding 95\% confidence interval (95\% CI).
Results Data of 1628 and 1035 participants were used to assess the accuracy in case finding of HADS-A and HADS-D,
respectively. According to the ROC analysis, the optimal cut-off was > 9 units for the HADS-A and > 7 units for the HADS-
D. The area under the ROC curve was 0.90 for HADS-A (95\% CI 0.88–0.91) and 0.84 for HADS-D (95\% CI 0.81–0.86).
Conclusions This study suggested that risk scores of anxious and depressive states above specific HADS cut-offs are useful in
identifying anxious and depressive states in cancer patients, and they may thus be applicable in clinical practice.
Keywords Accuracy . Cancer . Hospital Anxiety and Depression Scale . Oncology . Psychometrics . Receiver operating
characteristic curve
Introduction
The Hospital Anxiety and Depression Scale (HADS) [1–3] is
a well-known emotional distress self-report questionnaire, and
it is one of the most frequently used in oncology [4–6] as well
as in other physical health settings (e.g., cardiology, brain
injury, general medicine). Originally, it was designed to screen
emotional suffering of patients in non-psychiatric settings by
detecting the two most frequent distress components: anxiety
and depression. Since it is specific to patients with organic
diseases, HADS excludes somatic symptoms of emotional
distress (e.g., headache, weight loss, insomnia) that could be
caused by the illness itself (including its treatments) rather
than being emotional distress expressions [1, 2, 4, 5].
Furthermore, to improve sensitivity to medical conditions,
severely psychopathological symptoms are not covered by
HADS [1, 2, 4]. Thus, HADS is a measure of prolonged state
rather than trait [4], and it is not recommended in detecting
psychopathological disorders.
In spite of this, no agreement exists in literature on HADS
accuracy in case finding [7–9] also because of the large range
of cut-off rankings used by different authors [6, 7, 9]. In 2011,
Vodermaier and Millman [10] conducted a meta-analysis to
identify optimal, empirically derived HADS thresholds for
clinical decision-making. Analyzing data from 28 different
studies comparing HADS (entirely and/or in its subscales)
against a semi-structured or structured clinical interview as a
Maria Antonietta Annunziata and Barbara Muzzatti should be considered
joint first author.
* Maria Antonietta Annunziata
[email protected]
1 Unit of Oncological Psychology, Centro di Riferimento Oncologico
di Aviano (CRO), IRCCS, Via F. Gallini, 2, 33081 Aviano, PN, Italy
2 Unit of Cancer Epidemiology, Centro di Riferimento Oncologico di
Aviano (CRO), IRCCS, Aviano, Italy
https://doi.org/10.1007/s00520-019-05244-8
/ Published online: 19 December 2019
Supportive Care in Cancer (2020) 28:3921–3926
reference standard, they provided the most accurate (i.e., those
with best sensitivity and specificity) HADS cut-offs for any
mental disorder and depressive disorder alone.
The present research also addresses the issue of HADS
accuracy, though with an approach that differs from those
applied to previously published studies [7–10]. In fact, since
HADS is considered inadequate as screening tool for diagnos-
tic purposes in psychopathology, we compared it against other
(validated but lengthy and time-consuming administration)
screening tools able to detect prolonged negative emotional
states rather than against psychiatric assessment tools.
Moreover, since a previously published study [11], conducted
in a similar setting and on a large sample like the present one,
suggested to use HADS as a measure of anxious and depres-
sive states rather than as a global measure of emotional dis-
tress, we tested the accuracy of the two HADS subscales
against two different gold standard tools (one able to detect
anxious states, the other able to detect depressive states).
Methods
Participants
Study participants were consecutive adult cancer inpatients,
admitted to the same cancer institute for cancer treatments.
The eligibility criteria were the following: age ≥ 18 years
old; good understanding of the Italian language; absence of
mental disorders; absence of physical or sensory disabilities
that would interfere with completing the questionnaires; and
signed informed consent form.
Materials and procedure
The HADS together with the State Anxiety (Anxiety-S) sub-
scale of the State-Trait Anxiety Inventory, Form Y (STAI-Y)
[12], and the Center for Epidemiologic Studies Scale on
Depression (CES-D) [13] were used in this study.
HADS consists of two subscales: HADS-A, designed to
detect anxious states, and HADS-D, designed to detect
depressive states. Each subscale consists of seven items
with a 4-point ordinal response format. Scores ranges from
0 to 21 in each subscale, with higher scores indicating
higher levels of anxious or depressive state. Participants
answer each item thinking of how they felt and/or behaved
during the past week.
The STAI-Y is a questionnaire widely used to assess
anxiety in its trait and state components. Spielberger val-
idated the Italian version [14–16]. STAI-Y consists of 40
items (20 for trait anxiety and 20 for state anxiety), which
participants rate on a 4-point scale. Scores range from 20
to 80 for each scale (i.e., trait anxiety and state anxiety),
with higher scores indicating higher levels of state and
trait anxiety. In this study, only state subscale (STAI-S)
was administered and participants were requested to an-
swer each item thinking of how they felt and/or behaved
during the past week. Since it registers anxious states
rather than general anxiety disorder (or other psychopath-
ological disturbances), STAI-S was chosen as gold stan-
dard for HADS-A.STAI-S scores over 1.5 standard devi-
ation of the normative sample (depending on gender and
age) in the Italian manual [14], which were used to dis-
tinguish participants with anxious state from participants
without anxious state. Since a hospital stay may induce,
per sè, an anxiety state, we decided to raise the cut-off
from one standard deviation to 1.5, over the normative
score; concurrently, we fixed it a 1.5 standard deviation
rather than at 2 standard deviations over the normative
score to reduce false negatives.
The CES-D is a measure of depressive symptomatology
that consists of 20 items, which participants rate on a 4-point
scale, thinking of how they felt and/or behaved during the past
week. The results are graded on a 0–60-point scale and are
proportional to depressive state intensity. Italian validation
data were provided by Pierfederici et al. [17] and Fava [18]
for both the general population and general hospital inpatients.
Since it was developed to screen for depression in the general
population (therefore, primarily focused on depression affec-
tive components, such as depressed mood and feelings of
helplessness), and since it has adequate psychometric proper-
ties and has been widely used as screening tool in oncology [6,
19], CES-D was chosen as gold standard for HADS-D.
According to a previous research on depression in general
hospital inpatients [17], cut-off of 28+ was used to distinguish
participants in a depressive state from participants who were
not.
Potential participants were selected by consulting clinical
files. The three above-mentioned tools, together with the in-
formed consent form to participate in the study, were illustrat-
ed to each eligible participant by a psychologist. The forms
were autonomously filled out by participants in one occasion
and subsequently they were collected by the psychologist who
also debriefed the participants. Participants’ socio-
demographic and clinical data were collected by the psychol-
ogist consulting clinical files.
All participants gave their informed consent for inclusion
before they participated in the study. The study was conducted
in accordance with the Declaration of Helsinki, and the pro-
tocol was approved by the Ethics Committee of the Centro di
Riferimento Oncologico di Aviano (CRO) IRCCS (CRO-
2011-27).
Sample size
For HADS-A, we estimated the minimum sample size by
assuming the following parameters: a type I error of 5\%; a
3922 Support Care Cancer (2020) 28:3921–3926
power of 90\%; a hypothesized AUC of 0.8 for HADS-A; a
null hypothesis AUC of 0.7 for STAI-Y; and a ratio of
positive/negative patients of 1. The number of participants
required was 290 (145 cases and 145 controls). For HADS-
D, we estimated the minimum sample size by assuming the
following parameters: a type I error of 5\%; a power of 90\%; a
hypothesized AUC of 0.8 for HADS-D; a null hypothesis
AUC of 0.7 for CES-D; and a ratio of positive/negative pa-
tients of 1. The number of participants required was 290 (145
cases and 145 controls) [20].
A quota sampling method was used to gather the necessary
number of cases. All consecutive inpatients filled in the STAI-
Y and CES-D questionnaires in order to be classified as cases
or non-cases. Due to the sampling method used and the rela-
tively low number of detected cases by means of the two gold
standard tests, an elevated number of controls were detected
until the required number of cases was obtained. Thus, the
final sample consisted of a number of non-cases higher than
expected. According to this final sample size and to the ratio
of positive/negative patients observed, we recomputed the
power of the analysis a posteriori. The power was 99\%, with
a type I error of 1\%, for HADS-A, and 98\%, with a type I error
of 1\%, for HADS-D. Consequently, the power of the analysis
was higher than initially declared by the sample size
computation.
Statistical analyses
The ability of HADS-A and HADS-D to discriminate cancer
patients with or without respectively anxious or depressive
state was made by means of receiver operator characteristic
(ROC) curves [21]. The following indicators were calculated:
area under the curve (AUC), sensitivity and specificity, with
their corresponding 95\% confidence interval (95\% CI). The
Youden index was also calculated and, in conjunction with the
ROC curve, allowed to select the optimal cut-off for each test
[20]. In particular, the optimal cut-off of HADS-A and the
optimal cut-off of HADS-D, located in the most superior
top-left point on the ROC curve, were derived in each curve
from the point with the maximum Youden index that repre-
sented the maximized sensitivity and specificity [20]. All tests
were two-tailed and a p value < 0.05 was considered statisti-
cally significant. The statistical analyses were performed
using the SAS language program (Version 9.4, SAS Institute
Inc., Cary, NC).
Results
To reach the necessary sample size, we recruited 2121 inpa-
tients, of whom 1628 (76.8\%) provided complete subscale
HADS-A and STAI-S (necessary to verify the accuracy of
HADS_A) and 1035 (48.8\%) provided complete both
HADS-D and CES-D (necessary to verify the accuracy of
HADS-D). Table 1 summarizes the main socio-demographic
and clinical characteristics of the final sample.
HADS-A accuracy in case finding
The performance of HADS-A scores was evaluated according
to ROC curves. The optimal cut-off value of the HADS-Awas
> 9 units (Fig. 1). The AUC was 0.90 (95\% CI 0.88–0.91), p
value<0.001, with a sensitivity of 83.2\% (95\% CI 76.6–88.5)
and a specificity of 80.5\% (95\% CI 78.4–82.5).
When considering a cut-off value > 9 units of the HADS-A
subscale, 423 participants (26\% for the whole sample) result-
ed to be in the anxious state (see Table 2).
HADS-D accuracy in case finding
The performance of HADS-D was evaluated according to
ROC curves. The optimal cut-off value of the HADS-D was
> 7 units (Fig. 2). The AUC was 0.84 (95\% CI 0.81–0.86), p
value < 0.001, with a sensitivity of 72.9\% (95\% CI 64.9–80.0)
and a specificity of 79.0\% (95\% CI 76.2–81.6).
When considering a cut-off value > 7 units of the HADS-D
subscale, 292 participants (28.2\% of the whole sample) result-
ed to be in the depressive state (see Table 2).
Discussion
HADS seems to be the tool of choice for detecting negative
emotional states in cancer patients, thanks to its features (i.e.,
specific of medical settings; good psychometric properties;
brief; rapid administration; and good compliance) [1–6].
Although it is necessary to identify the optimal thresholds to
differentiate cases from non-cases (accuracy) for the HADS
reliable and valid use in both clinical practice and research, no
agreement exists in literature on this issue.
The present work contributes to define HADS accuracy in
case finding. However, it addresses this issue in an original
manner, i.e., by comparing it against other already validated
screening tools able to detect prolonged negative emotional
states rather than against psychiatric assessment tools useful in
psychopathological diagnosis.
According to the present results, the optimal cut-off
values were > 9 units for the HADS-A and > 7 units for
the HADS-D. These thresholds are different from the cut-
off provided for HADS in previous literature [6–10]. These
dissimilarities find an explanation in the different psycho-
logical constructs (emotional states vs. psychopathological
disorders) assessed by the tools used as reference standard.
For both established thresholds, sensibility and specificity
were adequate. Indeed, given that HADS is an emotional
state screening tool rather than a diagnostic one, a higher
3923Support Care Cancer (2020) 28:3921–3926
number of false positives (i.e., lower specificity) is accept-
able and finds its balance in a well-recognized feasibility.
In other words, with respect to the two employed gold
standard tools (STAI-S e CES-D), the HADS subscales,
anxiety, and depression, induce additional false positives,
but they are quicker in terms of administration and scoring,
more appropriate to capture patients emotional states in
medical settings, as a consequence they can be usefully
employed to detect anxious and depressive states in oncol-
ogy settings.
The employment of gold standard tools developed to detect
emotional states, rather than gold standard psychiatric
diagnostic tools, as well as the rigorous data analysis proce-
dure and the adequate statistical power represent the major
strengths of this study. Furthermore, our findings are easily
reproducible as they were obtained in a non-selected consec-
utive population of cancer inpatients. Mental disorders (i.e.,
documented presence and/or history of a psychiatric syn-
drome) were an exclusion criterion for the present study to
avoid their possible confusing role on detecting prolonged
negative emotional states associated with cancer and its
treatments.
A potential study limitation may consist of the non-
negligible percentage of the enrolled sample that had provided
Table 1 Socio-demographic and
clinical characteristics of sub-
samples used to test HADS-A
accuracy (N = 1628), and HADS-
D accuracy (N = 1035)
HADS-A accuracy test
(N = 1628)
HADS-D accuracy test
(N = 1035)
N \% N \%
Gender
Male 460 28.3 294 28.4
Female 1168 71.7 741 71.6
Education
Compulsory 642 39.4 418 40.4
Secondary 756 46.4 476 46.0
Post-secondary 230 14.1 141 13.6
Occupational status
Employed 919 56.4 584 56.4
Unemployed/homemaker/student 599 36.8 383 37.0
Missing datum 110 6.8 68 6.6
Marital status
Partnered 1234 75.8 788 76.1
Non-partnered 393 24.1 247 23.9
Missing datum 1 0.1 0 –
Cancer diagnosis
Oro-pharyngeal 47 2.9 32 3.1
Digestive apparatus 246 15.1 126 12.2
Respiratory system and intrathoracic organs 65 4.1 55 5.3
Breast 520 31.9 329 31.8
Genito-urinary 391 24.0 265 25.6
Hematologic 204 12.5 134 12.9
Others 155 9.5 94 9.1
Anxious state (STAI-Y)
Non-case 1462 89.8 – –
Case 166 10.2 – –
Depressive state (CES-D)
Non-case – – 891 86.1
Case – – 144 13.9
Mdn Range Mdn Range
Age (years) 53 18–83 53 21–83
3924 Support Care Cancer (2020) 28:3921–3926
invalid data and were consequently dropped from the study.
Unfortunately, reasons for providing incomplete or unfilled
questionnaires were not recorded; consequently, no specula-
tions on this point may be done.
Finally, the obtained results should be considered in the
light of the ongoing debate on the HADS dimensional struc-
ture [5, 6, 22–25], of the related item formulation, and of the
translation aspects [9, 26–28]. Concerning the dimensional
aspect of the HADS, a previous study [11], conducted on a
large sample with similar characteristics to the present one,
has shown that the bi-factorial structure is more appropriate
than the mono-factorial one [29]. The satisfactory balance
between sensitivity and specificity of both HADS-A and
HADS-D subscales, emerging in the present study, supports
their appropriateness in terms of content validity—therefore,
an adequate formulation of each single item—at least for the
Italian context. Future studies mirroring the methodology
herein reported, in linguistic-cultural contexts other than the
Italian one and in clinical settings different from the oncologic
one, will offer useful information for a further in-depth inves-
tigation of this aspect.
In conclusion, this study suggests that risk scores of anx-
ious and depressive states above specific cut-offs derived from
HADS may be useful in identifying anxious and depressive
states in cancer patients during clinical practice. In oncology,
emotional distress (in its main components of anxiety and
depression) is expected during the entire disease trajectory. It
is a source of suffering on its own, but it may also interfere
with treatment adherence, as well as with both health and
well-being. Its reliable and valid detection and monitoring
represent the first step toward a tailored comprehensive (bio-
psycho-social) care of cancer patients.
Table 2 Comparison of HADS-A vs. STAI-Y (gold standard) and HADS-D vs. CES-D (gold standard)
State
Anxious Depressive
STAI-Y (gold standard) HADS-A CES-D (gold standard) HADS-D
≤ 9 (non-cases) > 9 (cases) ≤ 7 (non-cases) > 7 (cases)
non-cases 1177 285 Non-cases 704 187
Cases 28 138 Cases 39 105
Sensitivity = 83.1 (95\% confidence interval 76.6–88.5)
Specificity = 80.5 (95\% confidence interval 78.4–82.5)
Sensitivity = 72.9 (95\% confidence interval 64.9–80.0)
Specificity = 79.0 (95\% confidence interval 76.2–81.6)
Fig. 2 Receiver operative characteristic (ROC) curve, corresponding area
under curve (AUC), sensitivity, and specificity of depressive state risk
score for distinguishing cases from non-cases (Aviano, Italy)
Fig. 1 Receiver operative characteristic (ROC) curve, corresponding area
under curve (AUC), sensitivity, and specificity of anxious state risk score
for distinguishing cases from non-cases (Aviano, Italy)
3925Support Care Cancer (2020) 28:3921–3926
Acknowledgments The authors wish to thank Ms. Luigina Mei for her
editorial assistance.
Compliance with ethical standards
All participants gave their informed consent for inclusion before they
participated in the study. The study was conducted in accordance with
the Declaration of Helsinki, and the protocol was approved by the Ethics
Committee of the Centro di Riferimento Oncologico di Aviano (CRO)
IRCCS (CRO-2011-27).
Conflict of interest The authors declare that they have no conflict of
interest.
References
1. Zigmond AS, Snaith RP (1983) The hospital anxiety and depres-
sion scale. Acta Psychiatr Scand 67:361–370
2. Snaith RP, Zigmond AS (1994) The Hospital Anxiety and
Depression Scale manual. Nelson, Windsor, NFER
3. Snaith RP (2003) The Hospital Anxiety and Depression Scale.
Health Qual Life Outcomes 1:29
4. Herrmann C (1997) International experiences with the Hospital
Anxiety and Depression Scale. A review of validation data and
clinical results. J Psychosom Res 42:17–41
5. Bjelland I, Dahl AA, Neckelmann D (2002) The validity of the
Hospital Anxiety and Depression Scale: an updated literature re-
view. J Psychosom Res 52:69–77
6. Vodermaier A, Linden W, Siu C (2009) Screening for emotional
distress in cancer patients: a systematic review of assessment instru-
ments. J Natl Cancer Inst 101(21):1464–1488
7. Mitchell AJ, Meader N, Symonds P (2010) Diagnostic validity of
the hospital anxiety and depression scale (HADS) in cancer and
palliative settings: a metaanalysis. J Affective Disord 126:335–348
8. Brennan C, Worrall-Davies A, McMillan D, Gilbody S, House A
(2010) The hospital anxiety and depression scale: a diagnostic
meta-analysis of case-finding ability. J Psychosom Res 69:371–378
9. Carey M, Noble N, Sanson-Fisher R, Mackenzie L (2012)
Identifying psychological morbidity among people with cancer
using the Hospital Anxiety and Depression Scale: time to revisit
first principles? Psycho-Oncol. 21:229–238
10. Vodermaier A, Millman RD (2011) Accuracy of the Hospital
Anxiety and Depression Scale as a screening tool in cancer patients:
a systematic review and meta-analysis. Support Care Cancer 19:
1899–1908
11. Annunziata MA, Muzzatti B, Altoè G (2011) Defining Hospital
Anxiety and Depression Scale (HADS) structure by confirmatory
factor analysis: a contribution to validation for oncological settings.
Ann Oncol 22:2330–2333
12. Spielberger CD (1983) State-trait anxiety inventory (form Y). Mind
Garden, Palo Alto (CA)
13. Radloff L (1977) The CES-D Scale: a self-report depression scale
for research in the general population. Appl Psych Meas 1:385–401
14. Spielberger CD (1989) S.T.A.I. (State-Trait-Anxiety Inventory).
Inventario per l’ansia di stato e di tratto. Forma Y. Organizzazioni
Speciali, Firenze
15. Pedrabissi L, Santinello M (1989) Verifica della validità dello STAI
forma Y di Spielberger. BPA. 191:11–14
16. Macor A, Pedrabissi L, Santinello M. (190). Ansia di stato e di
tratto: ulteriore contributo alla verifica della validità psicometrica
e teorica dello S.T.A.I. forma Y di Spielberger. Psicologia e Società.
15: 67–74
17. Pierfederici A, Fava GA, Munari F et al (1982) Validazione italiana
del CES-D per la misurazione della depressione. In: Canestrari R
(ed) Nuovi metodi in psicoterapia. Organizzazioni Speciali,
Firenze, pp 95–103
18. Fava GA (1983) Assessing depressive symptoms across cultures:
Italian validation of the CES-D self-rating scale. J Clin Psychol 39:
249–251
19. Hann D, Winter K, Jacobsen P (1999) Measurement of depressive
symptoms in cancer patients: evaluation of the Center for
Epidemiological Studies Depression Scale (CES-D). J Psychosom
Res 46:437–443
20. Hanley JA, McNeil BJ (1982) The meaning and use of the area
under a receiver operating characteristic (ROC) curve. Radiology.
143:29–36
21. Metz CE (1978) Basic principles of ROC analysis. Semin Nucl
Med 8:283–298
22. Rodgers J, Martin C, Morse R et al (2005) An investigation into the
psychometric properties of the Hospital Anxiety and Depression
Scale in patients with breast cancer. Health Qual Life Outcomes
3:41
23. Cosco TD, Doyle F, Ward M, McGee H (2012) Latent structure of
the Hospital Anxiety And Depression Scale: a 10-year systematic
review. J Psychosom Res 72:180–184
24. Norton S, Cosco T, Doyle F, Done J, Sacker A (2013) The Hospital
Anxiety and Depression Scale: a meta confirmatory factor analysis.
J Psychosom Res 74:74–81
25. Straat JH, van der Ark LA, Sijtsma K (2013) Methodological arti-
facts in dimensionality assessment of the Hospital Anxiety and
Depression Scale (HADS). J Psychosom Res 74:116–121
26. Maters GA, Sanderman R, Kim AY, Coyne JC (2013) Problems in
cross-cultural use of the Hospital Anxiety and Depression Scale:
“no butterflies in the desert”. PLoS One 8:e70975
27. Cameron IM, Scott NW, Adler M, Reid IC (2014) A comparison of
three methods of assessing differential item functioning (DIF) in the
Hospital Anxiety Depression Scale: ordinal logistic regression,
Rasch analysis and the Mantel chi square procedure. Qual Life
Res 23:2883–2888
28. Verdam MG, Oort FJ, Sprangers MA (2017) Item bias detection in
the Hospital Anxiety and Depression Scale using structural equa-
tion modeling: comparison with other item bias detection methods.
Qual Life Res 26:1439–1450
29. Saboonchi S, Wennman-Larsen A, Alexanderson K, Petersson ML
(2013) Examination of the construct validity of the Swedish version
of Hospital Anxiety and Depression Scale in breast cancer patients.
Qual Life Res 22:2849–2856
Publisher’s note Springer Nature remains neutral with regard to jurisdic-
tional claims in published maps and institutional affiliations.
3926 Support Care Cancer (2020) 28:3921–3926
Supportive Care in Cancer is a copyright of Springer, 2020. All Rights Reserved.
ORIGINAL ARTICLE
Impact of family caregivers’ awareness of the prognosis on their
quality of life/depression and those of patients with advanced
cancer: a prospective cohort study
EunKyo Kang1,2 & Bhumsuk Keam3 & Na-Ri Lee4 & Jung Hun Kang5 & Yu Jung Kim6 & Hyun-Jeong Shim7 &
Kyung Hae Jung8 & Su-Jin Koh9 & Hyewon Ryu10 & Jihye Lee11 & Jiyeon Choo11 & Shin Hye Yoo3 & Young Ho Yun1,11
Received: 12 December 2019 /Accepted: 20 April 2020
# Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract
Purpose A caregiver’s prognostic awareness can affect clinical decisions for the patient. The purpose of this study was to
examine the impact of family caregivers’ prognostic awareness on the quality of life (QOL) and emotional state of both patients
with advanced cancer and their caregivers.
Methods This prospective cohort study was conducted from December of 2016 to January of 2018. A total of 159 patients with
advanced cancer and an equal number of caregivers participated. The investigation tools used include the European Organization
for Research and Treatment of Cancer Quality of Life Questionnaire-C15-Palliative, the McGill Quality of Life Questionnaire,
and the Patient Health Questionnaire-9, and evaluation was performed at baseline, 3 months, and 6 months. Covariance analysis
with a general linear modeling was used to compare changes in quality of life scores according to the caregivers’ awareness of the
prognosis.
Results Mean patient overall QOL score increased in the group of caregivers who were aware of prognosis and decreased in the
caregivers who were not aware of the prognosis (p = 0.018). The changes over time in the patients’ QOL scores associated with
symptoms improved with caregiver awareness (pain, p = 0.017; dyspnea, p = 0.048; appetite loss, p = 0.045). The percentage of
depressed patients was smaller after 3 months in the group with caregivers aware of the prognosis (baseline to 3 months p =
0.028). Caregivers who did not understand their patients’ prognosis exhibited better existential well-being (p = 0.036), and the
incidence of depression was lower in this group at 3 months (p = 0.024).
Conclusion Caregivers’ prognostic awareness may improve the quality of life and mood in patients with advanced cancer;
however, this awareness may harm the quality of life and mood of the caregivers. These results may aid in developing in-
depth interventions regarding prognosis for both patients and their caregivers.
Keywords Advanced cancer patients . Caregiver’s awareness . Depression . Prognostic awareness . Quality of life
* Young Ho Yun
[email protected]; [email protected]
1 Department of Family Medicine, Seoul National University Hospital,
Seoul, South Korea
2 Institute for Public Health and Medical Service, Seoul National
University Hospital, Seoul, South Korea
3 Department of Internal Medicine, Seoul National University
Hospital, Seoul, South Korea
4 Department of Internal Medicine, Chonbuk National University
Medical School, Jeonju, South Korea
5 Department of Internal Medicine, Gyeongsang University Hospital,
Jinju, South Korea
6 Department of Internal Medicine, Seoul National University
Bundang Hospital, Seoul National University College of Medicine,
Seongnam, South Korea
7 Department of Internal Medicine, Chonnam National University
Medical School, Gwangju, South Korea
8 Department of Oncology, Asan Medical Center, University of Ulsan
College of Medicine, Seoul, South Korea
9 Department of Hematology and Oncology, Ulsan University
Hospital, Ulsan University College of Medicine, Ulsan, South Korea
10 Internal Medicine, Chungnam National University College of
Medicine, Chungnam, South Korea
11 Department of Medical Informatics, Seoul National University
College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, South Korea
https://doi.org/10.1007/s00520-020-05489-8
/ Published online: 6 May 2020
Supportive Care in Cancer (2021) 29:397–407
Introduction
As the life expectancy of patients with advanced cancer in-
creases [1, 2], the role of caregivers who provide comprehen-
sive support is becoming more important. Family caregivers
deal with the complications of long-term management of can-
cer treatment [3], which can impact the quality of life of the
patients as well as those who care for them. Supporting cancer
patients is associated with considerable physical, mental, and
financial burdens for the caregiver [4, 5].
The role of caregivers in treatment decisions and the long-
term care of the patient are becoming increasingly important
[6]. Studies indicate that caregivers of cancer patients with poor
prognosis desire more prognostic information and feel that dis-
closure of information about incurable cancer is very important
[7, 8]. Caregivers aware of a patient’s incurable illness might
want to prevent the patient from learning about their prognosis
[9]. In addition, the patient’s caregiver may participate in the
discussions between the patient and their physician, or assist the
patient with accepting a poor prognosis [10, 11]. Patients and
caregivers may differ in their treatment preferences and deci-
sions about end-of-life care, which may be influenced by the
awareness that the cancer is incurable [12, 13]. Furthermore,
according to previous studies, the caregiver’s perception of an
incurable disease may affect the intimacy between patient and
caregiver, leading to distress [14]. Because of these findings,
disclosure of the prognosis to caregivers remains controversial.
Studies have found that patients with advanced cancer who
are aware of their prognosis are more likely to experience
decreased quality of life and increased depression [15–18],
which are both associated with poor survival [17]. On the
other hand, research suggests that disclosing prognosis to pa-
tients reduced their stress and promoted psychological and
emotional well-being [19–21]. Few studies are investigating
the impact of caregivers’ awareness of prognosis on their
quality of life, however, or how this awareness affects patient
quality of life [18].
Considering the important role of the family in managing
cancer long-term, it is important to investigate the effect of the
caregivers’ awareness of prognosis on the quality of life and
emotional state of patients. Additionally, a family member’s
cancer diagnosis may lead to depression and impaired quality
of life for caregivers [22, 23]. It is also important to understand
the impact of caregivers’ prognostic awareness on their qual-
ity of life and emotional state.
The purpose of this study was to investigate the effect of
caregivers’ prognostic awareness on their quality of life/
emotional state and the QOL of the patients. Considering the
impact of prognostic awareness on the quality of life and emo-
tional state of caregivers and patients [10, 24, 25], the results
of this study may help identify the effects of caregivers’ lack
of prognostic awareness on both the caregivers and patients
with incurable cancer.
Material and methods
Study design
This prospective Korean nationwide cohort study was con-
ducted across three periods between December 17, 2016,
and August 17, 2018. Patients and their caregivers who were
(1) diagnosed with advanced cancer and (2) aged ≥ 19 years
were eligible for inclusion in this study. Physicians evaluated
the patients’ understanding of the purpose of the study and
enrolled only those with insight into their disease. The physi-
cian enrolled patients only if they had advanced cancer and if
their life expectancy was determined to be less than 1 year. All
patients and caregivers understood the purpose and methods
of the study and provided informed consent to participate, and
the institutional review boards of all 21 participating hospitals
approved the protocol. This trial is registered with
ClinicalTrials.gov (NCT03222258).
Data collection
The patients’ primary cancer site, Eastern Cooperative
Oncology Group (ECOG) performance status, and therapeutic
status were collected through a medical record review. The
therapeutic status was classified as follows: (1) receiving stan-
dard chemotherapy, (2) at an intermittent stage of standard che-
motherapy, (3) receiving additional chemotherapy following
standard chemotherapy, and (4) at the stage of any chemother-
apy yet expected to survive more than 6 months but for less
than 1 year. Demographic data and information regarding
awareness of prognosis, quality of life, and emotional status
were obtained through patient interviews. For data uniformity,
research nurses conducted face-to-face patient interviews. The
questionnaires required approximately 20 min to complete. The
patients and their caregivers were followed up at 3 months and
6 months through direct meetings with the research assistants in
the inpatient facility or outpatient clinic. If the patient or care-
giver was lost to follow-up (Fig. 1), we checked with the phy-
sician regarding the patient’s survival. In Korea, patients’ sur-
vival or death data are managed by both the National Health
Insurance Service and the National Statistical Office, so physi-
cians can verify patient survival. If the patient was alive but not
contacted, they were classified as “unable to contact.”
Measurements
The demographic information included age, sex, educational
status, marital status, religion, and place of residence. The
patients’ and caregivers’ awareness of the prognosis was
assessed through self-administered questionnaires at baseline.
The patients and their caregivers were asked the following
question: “Do you think your illness (or the patient’s illness)
will be cured?” They responded by selecting one of the
398 Support Care Cancer (2021) 29:397–407
following options: (1) my (or the patient’s) cancer will be
cured, (2) my (or the patient’s) cancer might be cured if the
treatments are effective, (3) my (or the patient’s) cancer cannot
be cured, but treatment inhibits cancer progression and ex-
tends survival, (4) my (or the patient’s) cancer cannot be
cured, and cancer treatment is no longer effective, or (5) I do
not know [26]. Awareness of the prognosis was defined as
“present” for response options 3 and 4 and “absent” for re-
sponse options 1, 2, and 5.
Quality of life was measured using two indices: the
European Organization for Research and Treatment of
Cancer Quality of Life Questionnaire-C15-Palliative
(EORTC QLQ-C15-PAL) [27] and the McGill Quality of
Life (MQOL) questionnaire [28]. We assessed patients with
both the EORTC QLQ-C15-PAL and MQOL questionnaires
and assessed the caregivers with the MQOL questionnaire
only. The EORTC QLQ-C15-PAL is a questionnaire that in-
cludes only 15 of the 30 items in the EORTC QLQ-C30 ques-
tionnaire for palliative care. The EORTC QLQ-C15-PAL as-
sesses physical and emotional functioning, overall quality of
life, and seven symptoms related to quality of life (i.e., fatigue,
nausea/vomiting, dyspnea, pain, insomnia, appetite loss, and
constipation). High scores related to functioning and overall
quality of life indicate a better quality of life. High symptom
scores indicate increased severity of symptoms.
The MQOL is a multidimensional questionnaire that as-
sesses quality of life. In this study, we measured the quality of
life associated with two aspects of existential well-being and
social support. High scores in the MQOL questionnaire
represent a higher quality of life. Cronbach’s alpha coefficients
of the EORTC QLQ-C15-PAL for multi-item scales ranged
from 0.67 (pain) to 0.85 (emotional functioning) [29]. In this
study, the Cronbach’s alpha coefficients of the EORTC QLQ-
C15-PAL for multi-item scales ranged from 0.84 to 0.87. The
Korean version of the MQOL questionnaire presented a
moderate-to-high internal consistency (Cronbach’s alpha,
0.62–0.90) and a good concurrent validity [30]. The
Cronbach’s alpha coefficients of the MQOL questionnaire
ranged from 0.73 to 0.84 (patients) and 0.67 to 0.80
(caregivers) in this study.
The emotional state of patients and caregivers was assessed
through the Patient Health Questionnaire-9 (PHQ-9) [31].
Consistent with the Diagnostic and Statistical Manual of
Mental Disorders (5th Edition), the PHQ-9 is a self-
reporting questionnaire for the diagnosis of depression [32].
Participants with a PHQ-9 score of ≥ 10 were diagnosed with
depression. The Korean version of the PHQ-9 questionnaire
showed a high internal consistency (Cronbach’s alpha was
0.852) [33]. In this study, the prevalence of depression was
presented as the percentage of depressed participants. The
Cronbach’s alpha coefficients of the PHQ-9 questionnaire
were 0.90 (patients) and 0.91 (caregivers).
Statistical analyses
The items in the MQOL questionnaire were scored using a
scale from 0 to 10. According to the EORTC scoring manual,
the items in the EORTC questionnaire were scored using a
Fig. 1 The flow of participants
through each stage of the study
399Support Care Cancer (2021) 29:397–407
linear scale from 0 to 100 [27]. The primary outcome of this
study was a change in patients’ quality of life according to the
caregivers’ prognostic awareness. Covariance analysis with
general linear modeling was used to compare changes in qual-
ity of life scores between the groups. To analyze how caregiv-
er awareness affects a patient’s quality of life and depression, a
multivariate general linear model (GLM) was fit using an
unstructured covariance matrix for the outcome variables
(i.e., quality of life × time points) with role (i.e., patient/care-
giver) as the within-subjects factor and type of tumor, baseline
score, and patients’ ECOG performance as the covariates. The
baseline score and ECOG performance status were adjusted.
Changes from baseline to 3 months and changes over time
were measured (as the difference in time effect [awareness
× time] considering the correlation of repeated observa-
tions for each participant). In the EORTC questionnaire,
one item was missing from the questionnaire of two pa-
tients at baseline, and the missing items were handled as
recommended in the scoring manual. A multiple imputed
data set was produced—excluding patients who expired—
via a multiple imputation method. This study used regres-
sion methods and propensity scores for imputation.
Imputed values were used for covariance analysis. We
used the STATA version 14.2 (STATA, College Station,
TX) software for all statistical analyses. We considered a
two-sided P value < 0.05 as significant.
Results
Study participants
The demographic characteristics of the participants are
shown in Table 1. Of the 159 caregivers who responded,
101 (63.5\%) were aware of the patients’ prognosis and 58
(36.5\%) were unaware. The majority of the caregivers
were female, whereas the majority of the patients were
male. The mean age of the patients was higher than that
of the caregivers; however, the age difference between the
aware and unaware groups was not statistically significant.
Both the patients and the caregivers had an average level of
education (below a high school graduate). More caregivers
than patients were employed. Notably, there was no signif-
icant difference between the two groups in educational sta-
tus, occupation, religion, or marital status. According to
the awareness of patient caregivers, there was no signifi-
cant difference in monthly income and area of residence.
Lung cancer was the most common primary cancer report-
ed in both groups. In terms of ECOG performance status,
ECOG 1 was the most commonly reported grade (48.3\%)
in the aware group. However, in the unaware group, ECOG
3 was the most commonly observed grade (49.5\%). The
difference between the groups in ECOG status was signif-
icant (p = 0.004).
Changes in the quality of life of patients over time
(EORTC QLQ-C15-PAL)
There was no statistically significant difference in the change
of quality of life scores related to physical and emotional
functioning between the aware and unaware caregiver groups
(Fig. 2). However, the mean overall quality of life score for
patients increased from 50.5 to 58.3 in the group with care-
givers who were aware of the prognosis, whereas it decreased
from 55.4 to 53.7 in the group of caregivers with no aware-
ness. There was a significant difference between the groups
over time (p = 0.018). There were significant differences be-
tween the groups in changes over time in the quality of life
scores related to symptoms. Regarding pain, the mean score
decreased from 32.6 to 24.5 in the aware group, whereas it
increased from 26.9 to 32.9 in the unaware group (from base-
line to 3 months p = 0.035, changes over time p = 0.017). For
dyspnea, the score decreased from 25.8 to 15.4 in the aware-
ness group and the unaware group showed limited change
(from 28.1 to 28.0) (changes over time p = 0.048). The mean
score related to appetite loss decreased in the aware group
from 45.7 to 36.8, whereas the mean score reported in the
unaware group increased from 34.8 to 39.6 (from baseline to
3 months p = 0.033, over time p = 0.045).
Changes in the quality of life of patients
and caregivers over time (MQOL)
Between baseline and 6 months, the patients’ existential well-
being mean score significantly increased in the group with no
awareness, whereas it significantly decreased in the group
with awareness (changes over time p = 0.031; Fig. 3a). The
changes in the mean MQOL existential well-being scores
were inversely correlated with a caregiver’s prognostic aware-
ness compared with the EORTC QLQ-C15-PAL score. We
performed further analyses in four groups, including patient
awareness (Appendix, Fig. 5). The results revealed that QOL
significantly decreased when both the caregiver and patient
were aware of the prognosis, compared with their QOL when
both the caregiver and patient remained unaware of the prog-
nosis (over time p = 0.048). The difference in the social sup-
port scores between the two groups was not significant.
Caregivers who were unaware of the patients’ prognosis had
better quality of life scores over time, in terms of existential
well-being, as compared with caregivers who were aware of
the illness (over time p = 0.036; Fig. 3c). Regarding social
support, the scores of both groups decreased over time, but
there was no significant difference between the groups.
400 Support Care Cancer (2021) 29:397–407
Changes in the proportion of depressed patients
and caregivers
The proportion of depressed patients in the aware patient
group decreased from 42.9 to 24.1\% after 3 months, and the
mean score was 37.4\% after 6 months. The proportion of
depressed patients in the unaware patient group decreased
from 33.4 to 28.5\% (at 3 months) and 31.7\% (at 6 months).
The difference between the two groups was significant from
baseline to 3 months (p = 0.028; Fig. 4a). In the aware care-
giver group, the proportion of depressed caregivers increased
from 19.6 (baseline) to 56.6\% (3 months) and 70.1\%
(6 months). In the unaware caregiver group, the proportion
of depressed caregivers increased from 32.7 (baseline) to
45.5\% (3 months) and 61.9\% (6 months). The changes over
time demonstrated significant differences between the two
caregiver groups (from baseline to 3 months p = 0.003, over
time p = 0.024; Fig. 4b).
Discussion
The results of this study indicate that caregivers’ prognostic
awareness is associated with improvements in patient quality
of life, and when caregivers are not aware of the prognosis, there
is an associated decrease or lack of significant change in pa-
tients’ quality of life. However, the quality of life of caregivers
tended to decrease with increased awareness of their patients’
incurable disease. In the aware caregiver group, the patients’
depression improved, but the caregivers’ depression worsened.
Previous research has shown that caregivers’ positive atti-
tudes about disclosure of disease prognosis were inversely
Table 1 Sociodemographic characteristics of the participantsa
Caregivers Patients
Characteristic Without prognostic
awareness (N = 58)
With prognostic
awareness (N = 101)
Without prognostic
awareness (N = 58)
With prognostic
awareness (N = 101)
N (\%)
Sex Male 12 (20.7) 35 (34.6) 42 (72.4) 61 (60.4)
Female 46 (79.3) 66 (65.4) 16 (27.6) 40 (39.6)
Age (years) Mean (SD) 54.4 (12.5) 51.3 (14.2) 59.9 (11.0) 63.7 (9.9)
Educational status ≤ High school 44 (75.9) 60 (60.0) 46 (79.3) 81 (80.2)
College or
higher
14 (24.1) 40 (40.0) 12 (20.7) 20 (19.8)
Job status Employed 35 (60.3) 73 (73.0) 10 (17.2) 8 (7.9)
Unemployed 23 (39.7) 27 (27.0) 48 (82.8) 93 (92.1)
Religion Yes 17 (29.3) 39 (39.0) 39 (67.2) 58 (57.4)
No 41 (70.7) 61 (61.0) 19 (32.8) 43 (42.6)
Marital status Not married 8 (13.8) 15 (15.0) 8 (13.8) 17 (16.8)
Married 50 (86.2) 85 (85.0) 50 (86.2) 84 (83.2)
Monthly income
(USD)
< 2000 30 (53.6) 45 (45.5) 32 (58.2) 64 (64.6)
2000–3999 20 (35.7) 25 (25.2) 16 (29.1) 17 (17.2)
≥ 4000 6 (10.7) 29 (29.3) 7 (12.7) 18 (18.2)
Residence Rural/suburban 40 (69.0) 70 (70.0) 14 (24.1) 25 (24.8)
Urban 18 (31.0) 30 (30.0) 44 (75.9) 76 (75.2)
Primary cancer site Breast 13 (22.4) 8 (8.0)
Colon 3 (5.2) 13 (13.0)
Stomach 7 (12.1) 21 (21.0)
Pancreato-biliary 8 (13.8) 17 (17.0)
Blood 2 (3.5) 8 (8.0)
Lung 25 (43.1) 33 (33.0)
ECOG status 0 8 (13.8) 2 (2.0)
1 28 (48.3) 37 (36.6)
2 4 (6.9) 7 (6.9)
3 18 (31.0) 50 (49.5)
4 0 (0.0) 5 (5.0)
a In some factors, there may be a missing value due to nonresponse of respondents
401Support Care Cancer (2021) 29:397–407
a Physical Functioning b Emotional Functioning
c Overall quality of life d Fatigue
e Nausea/Vomiting f Pain
g Dyspnea h Insomnia
i Appetite loss j Constipation
402 Support Care Cancer (2021) 29:397–407
associated with low quality of life scores in their patients [34].
However, that cross-sectional study did not examine the care-
givers’ actual awareness of the prognosis. In this study, the
quality of life scores improved in patients whose caregivers
were aware of their prognosis. Moreover, the prevalence of
depression tended to decrease in the short term in patients
whose caregivers were aware of the prognosis.
Although previous studies examined the effect of patient
awareness on the quality of life and depression of patients and
caregivers [35, 36], no studies have yet investigated the effect
of caregivers’ prognostic awareness on quality of life and
depression among patients and caregivers. Previous studies
have shown that the caregiver’s perception of prognosis was
associated with patient symptoms and quality of life [37],
hospital readmission [38], and survival [39–41]. In addition,
caregiver depression or poor health status affected the self-
reported quality of patient care [42]. Although the mean
MQOL score of patients was inversely correlated with the
awareness of caregivers and the EORTC QLQ-C15-PAL
score was not, in the analysis that included patient perception,
patient awareness may have influenced quality of life
(Appendix, Fig. 5). Based on these results, physicians should
disclose information about the illness and actively communi-
cate with the families of those patients with advanced cancer.
Caregivers may be more vulnerable to depression or anxi-
ety than patients [43]. The negative impact of the burden as-
sociated with caregiving on caregivers’ physical and psycho-
logical health and quality of life has been well recognized [44,
45]. Many caregivers want sufficient information on their pa-
tient’s illness; however, the effects of this knowledge have not
been adequately investigated [43, 46, 47]. In this study, in-
creased caregiver prognostic awareness was associated with
decreased emotional state and quality of life scores for the
caregivers. These findings highlight the need to investigate
the negative effects of prognostic awareness of incurable
a Existential Well-being (Patients) b Social support (Patients)
c Existential Well-being (Family caregivers) d Social support (Family caregivers)
Fig. 3 Least square means of quality of life (McGill Quality of Life
Questionnaire; the McGill Quality of Life Questionnaire (MQOL) uses
existential well-being, and social support) according to family caregiver’s
awareness, at all three time points. a, b Patient’s quality of life. c, d
Family caregiver’s quality of life (adjusting for the baseline score and
ECOG status). All of the response categories are based on numerical
scales from 0 to 10, with verbal anchors at the beginning and end of the
scales
�Fig. 2 Least square means of quality of life (EORTC QLQ-C15-PAL; the
items of the EORTC questionnaire were scored using a scale from 0 to
100) according to family caregiver’s awareness of the prognosis at base-
line, 3 months, and 6 months (adjusting for the baseline score and ECOG
status). a Physical functioning. b Emotional functioning. c Overall quality
of life. d Fatigue. e Nausea/vomiting. f Pain.g Dyspnea. h Insomnia. i
Appetite loss. j Constipation
403Support Care Cancer (2021) 29:397–407
illness on the caregiver’s emotional state and quality of life.
Tailored interventions should be developed based on this ev-
idence. Caregivers’ awareness may have a positive impact on
patients’ quality of life and emotional state. Therefore, disclo-
sure of the prognosis to caregivers through appropriate inter-
ventions is important for both caregivers and patients.
The methodological advantages of this study are its pro-
spective design and nationwide cohort. However, this study
exclusively recruited patients from hospitals in Korea.
Therefore, our findings may not be generalizable to diverse
populations in different geographical areas. Using validated
questionnaires, we were able to avoid information bias.
However, selection bias in the individuals who participated
in the study is possible. However, since there are similar
sociodemographic and clinical characteristics among those
who participated in the study and those who did not, a strong
selection bias seems unlikely. Another potential limitation of
this study is its relatively short follow-up period (6 months).
Further studies are warranted to assess the long-term effects
associated with caregivers’ awareness. High follow-up loss
rates could also be a limitation of this study. We performed
multiple imputations during data analysis (17 participant’s da-
ta at 3-month follow-up analysis and 34 participant’s data at 6-
month follow-up analysis). As this study was conducted on
advanced cancer patients, the follow-up loss rate was high.
Although we made many efforts to follow-up with patients
and caregivers, many subjects experienced various end-of-
life difficulties, resulting in follow-up losses that may bias
the results.
In conclusion, the results of this prospective cohort study
involving patients with advanced cancer and their caretakers
indicate that caregivers’ prognostic awareness was linked to
improved patient quality of life. At the same time, the quality
of life of the caregivers aware of the prognosis decreased. In
addition, caregivers’ prognostic awareness was associated
with improved rates of depression in the patients, but it was
also associated with a deterioration in the caregivers’ emotion-
al state. These results may help predict the impact of prognos-
tic disclosure and develop effective interventions regarding
incurable illnesses for both patients and their caregivers.
Author contributions All authors contributed to the study conception and
design. Material preparation and data acquisition were performed by
Bhumsuk Keam, Na-Ri Lee, Jung Hun Kang, Yu Jung Kim, Hyun-
Jeong Shim, Kyung Hae Jung, Su-Jin Koh, Hyewon Ryu, Jihye Lee,
Jiyeon Choo, Shin Hye Yoo, and Young Ho Yun. Statistical analysis
was performed by EunKyo Kang and Young Ho Yun. Interpretation of
data and analysis were done by EunKyo Kang, Young Ho Yun, Jihye Lee,
and Jiyeon Choo. The first draft of the manuscript was written by EunKyo
Kang and Young Ho Yun. All authors critically revised the manuscript.
All authors read and approved the final version for publication.
Funding information This work was supported by a grant from the Korea
Health Technology R&D Project through the Korea Health Industry
Development Institute (KHIDI), funded by the Ministry of Health and
Welfare, Republic of Korea (grant number: HC15C1391). The funders
had no role in the design and conduct of the study; collection, manage-
ment, analysis, and interpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit the manuscript for
publication.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Ethical approval All procedures executed in this study were performed
in accordance with the ethical standards of the institutional and/or nation-
al research committee and with the 1964 Declaration of Helsinki and its
later amendments or comparable ethical standards. The written informed
consent forms and the study were approved by the institutional review
board at each hospital (IRB No. 1602-142-745). Informed consent was
obtained from all participants in this study.
a Depression (Patients) b Depression (Family caregivers)
Fig. 4 Proportions of depressed patients (the …
Literature Review on Cancer & Depression
You are to write a 1050 to 1750 word literature review (in addition to the title page and references page) on the articles you selected for Week 2, synthesizing the findings in the articles that you found on your topic. You may incorporate other articles or references to support your discussion, as needed. Use APA citation and reference guidelines.
What is a literature review?
A literature review is a synthesis and critique of the published research in a given area of research. Your focus is on the findings of the studies you are exploring – their methods, approach, results, and implications – rather than the broad topic overall. It should synthesize findings in specific areas. Thus, you should look for themes in the range of articles and write about them as you group common themes.
Synthesize the material you found. In other words, find connected themes in the different areas you cover. Occasionally you might discuss individual articles, but only if the article is very unique and no other article has similar findings. The synthesis should focus strictly on existing, published research.
What else should you include besides a synthesis of research?
Be sure to include in your review other potential areas that still need to be explored. What unanswered questions are there? What holes are in the research that you have not yet found answers to? What contradictions are in the research will you seek to explore?
Examples of Synthesized Findings for Literature Review:
College students were found to have a large number of conflicts with roommates (Darsey, 2003; Smith, 2001; Yarmouth, 2005). Researchers also found that roommate conflicts were most frequent during the first semester of college (Lotspiech, 2004; Nominskee, 2001; Zackarov, 2000). Morissey (2004) found a reduction of roommate conflicts continued as students progressed from freshman to seniors, with seniors having the fewest roommate conflicts. However, Ellensworth (2001) found no correlation with year in school and frequency of roommate conflict. The contradiction between Ellensworth’s and Morissey’s findings suggest that additional research is needed in this area.
Ellensworth’s (2001) research was strictly quantitative, lacking a full picture of the contexts or reasons for the specific conflicts. It asked people to mark the frequency of their conflicts and types of people with whom they typically disputed. Morissey (2004) conducted interviews that allowed participants to provide an explanation for the reasons for the conflicts, and the contexts (dorm roommates, apartment roommates, house roommates, etc.). However, she interviewed far fewer people than Ellensworth surveyed.
Combining Ellensworth’s surveys with Morissey’s interview questions and utilizing a research team to increase the number of interviews could provide more details about the conflicts and contexts, and allow us to further look into the question of year in school and conflict behavior.
DeSoto (2005) and Craig (2004) found that most students lack an understanding of the term “binge drinking.” This research finding suggests that earlier investigations that utilized the words “binge drinking,” as an identifier for students have limited use. It also suggests that researchers should be very careful when using terms to make sure that research participants fully understand the terms being used.
Howard Sharp, K. M., Fisher, R. S., Clark, O. E., Dunnells, Z. D. O., Murphy, L. K., Prussien, K. V., Vannatta, K., Compas, B. E., & Gerhardt, C. A. (2020). Long-term trajectories of depression symptoms in mothers of children with cancer. Health Psychology, 39(2), 89–98. https://doi-org.proxy-library.ashford.edu/10.1037/hea0000826
Niedzwiedz, C. L., Knifton, L., Robb, K. A., Katikireddi, S. V., & Smith, D. J. (2019). Depression and anxiety among people living with and beyond cancer: a growing clinical and research priority. BMC Cancer, 19(1), 943. https://doi-org.proxy-library.ashford.edu/10.1186/s12885-019-6181-4
Saracino, R. M., Aytürk, E., Cham, H., Rosenfeld, B., Feuerstahler, L. M., & Nelson, C. J. (2020). Are we accurately evaluating depression in patients with cancer? Psychological Assessment, 32(1), 98–107. https://doi-org.proxy-library.ashford.edu/10.1037/pas0000765.supp (Supplemental)
Kang, E., Keam, B., Lee, N.-R., Kang, J. H., Kim, Y. J., Shim, H.-J., & Jung, K. H. (2021). Impact of family caregivers’ awareness of the prognosis on their quality of life/depression and those of patients with advanced cancer: a prospective cohort study. Supportive Care in Cancer, 29(1), 397. https://doi-org.proxy-library.ashford.edu/10.1007/s00520-020-05489-8
Annunziata, M. A., Muzzatti, B., Bidoli, E., Flaiban, C., Bomben, F., Piccinin, M., Gipponi, K. M., Mariutti, G., Busato, S., & Mella, S. (2020). Hospital Anxiety and Depression Scale (HADS) accuracy in cancer patients. Supportive Care in Cancer, 28(8), 3921–3926. https://doi-org.proxy-library.ashford.edu/10.1007/s00520-019-05244-8
Long-Term Trajectories of Depression Symptoms in Mothers of Children
With Cancer
Katianne M. Howard Sharp
The Research Institute at Nationwide Children’s Hospital,
Columbus, Ohio, and The Ohio State University
Rachel S. Fisher, Olivia E. Clark,
and Zackery D. O. Dunnells
The Research Institute at Nationwide Children’s Hospital,
Columbus, Ohio
Lexa K. Murphy and Kemar V. Prussien
Vanderbilt University
Kathryn Vannatta
The Research Institute at Nationwide Children’s Hospital,
Columbus, Ohio, and The Ohio State University
Bruce E. Compas
Vanderbilt University
Cynthia A. Gerhardt
The Research Institute at Nationwide Children’s Hospital,
Columbus, Ohio, and The Ohio State University
Objective: To identify trajectories of depression symptoms in mothers of children with cancer from
diagnosis/relapse through 5 years and examine maternal factors at diagnosis/relapse predicting member-
ship in these trajectories. Method: Mothers (n � 327; Mage � 37.6 years, SD � 7.7 years; 85.9\% White)
reported depression symptoms near the time of their child’s diagnosis/relapse and then again at 1-, 3-, and
5-years postdiagnosis/relapse. Mothers also reported perceived stress, coping (primary control, secondary
control, and disengagement coping), and spirituality near the time of diagnosis. Latent class growth
analysis was used to identify latent trajectories of depression symptoms, and a 3-step multinomial logistic
regression tested covariate predictors of membership in the trajectories. Results: Three trajectories were
identified: “low depression symptoms” (63.3\%), “moderate depression symptoms” (31.5\%), and “high
depression symptoms” (5.2\%). Mothers who used more primary and secondary control coping were more
likely to be in the low depression symptom trajectory as compared with the moderate (OR � 1.64, p �
.024 and OR � 1.38, p � .013, respectively) or high trajectories (OR � 1.99, p � .008 and OR � 1.81,
p � .001, respectively). Conclusions: Although mothers of children with cancer generally displayed
improved mental health further from diagnosis, mothers with more depression symptoms after diagnosis/
relapse displayed substantial stability in depression symptoms over the 5 years. Mothers of children with
cancer may benefit from early screening of mental health and coping strategies, as well as interventions
to bolster effective coping for those with elevated depression symptoms.
Keywords: coping, mothers’ depressive symptoms, latent class growth analysis, pediatric cancer, trajectories
With improved survival rates for pediatric cancer (DeSantis et
al., 2014), it is increasingly important to understand the long-term
effects of pediatric cancer on families. A child’s diagnosis of
cancer affects the entire family, particularly parents. Parental dis-
This article was published Online First December 2, 2019.
X Katianne M. Howard Sharp, Center for Biobehavioral Health, The
Research Institute at Nationwide Children’s Hospital, Columbus, Ohio, and
Department of Pediatrics, The Ohio State University; Rachel S. Fisher,
Olivia E. Clark, and Zackery D. O. Dunnells, Center for Biobehavioral
Health, The Research Institute at Nationwide Children’s Hospital; Lexa K.
Murphy and Kemar V. Prussien, Department of Psychology and Human
Development, Vanderbilt University; Kathryn Vannatta, Center for Biobe-
havioral Health, The Research Institute at Nationwide Children’s Hospital,
and Department of Pediatrics, The Ohio State University; Bruce E. Com-
pas, Department of Psychology and Human Development, Vanderbilt
University; Cynthia A. Gerhardt, Center for Biobehavioral Health, The
Research Institute at Nationwide Children’s Hospital, and Department of
Pediatrics, The Ohio State University.
Katianne M. Howard Sharp is now at the Department of Psychology, St.
Jude Children’s Research Hospital, Memphis, Tennessee. Rachel Fisher is
now at the Department of Psychology, Oklahoma State University. Olivia
Clark is now at the Department of Psychology, Loyola University Chicago.
Zackery Dunnells is now at Anne and Henry Zarrow School of Social
Work, The University of Oklahoma.
This research was supported by a grant from the National Institutes of
Health (R01CA118332). Portions of this work were presented at the Society of
Pediatric Psychology Annual Conference, Orlando, Florida (April 2018). We
thank the families who generously participated in this work.
Correspondence concerning this article should be addressed to Cynthia A.
Gerhardt, Center for Biobehavioral Health, The Research Institute at Nation-
wide Children’s Hospital, Room FB3135, 700 Children’s Drive, Columbus,
OH 43205-2696. E-mail: [email protected]
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
Health Psychology
© 2019 American Psychological Association 2020, Vol. 39, No. 2, 89 –98
ISSN: 0278-6133 http://dx.doi.org/10.1037/hea0000826
89
tress after diagnosis has critical implications for parents’ physical
health, family/marital adjustment, and child adjustment (Kearney,
Salley, & Muriel, 2015). However, relatively little is known about
which parents are at risk for long-term adjustment problems and
what protective factors may mitigate their distress. By better
understanding trajectories of parent adjustment and factors that
predict risk for long-term distress, interventions may be introduced
earlier to foster resilience.
Although many parents of children with cancer are resilient
(Kearney et al., 2015; Phipps et al., 2015), a subset experience
marked distress or mental health problems (e.g., anxiety, depres-
sion), even years after diagnosis (Compas et al., 2015; Creswell,
Wisk, Litzelman, Allchin, & Witt, 2014; Kazak et al., 2015;
Norberg & Boman, 2008; Wijnberg-Williams, Kamps, Klip, &
Hoekstra-Weebers, 2006). Parents of children with cancer also
report more emotional distress than healthy comparison parents
(Creswell et al., 2014; Maurice-Stam, Oort, Last, & Grootenhuis,
2008; Norberg & Boman, 2008; Pai et al., 2007; Wijnberg-
Williams et al., 2006). Specifically, parents of children with cancer
may be more likely to experience prolonged clinically elevated
depression symptoms than either anxiety or posttraumatic stress
symptoms (Katz et al., 2018). In contrast with parents of healthy
children, parents of children with cancer display more depression
symptoms at multiple time points from diagnosis through 5 years
postdiagnosis (Katz et al., 2018; Norberg & Boman, 2008;
Vrijmoet-Wiersma et al., 2008; Wijnberg-Williams et al., 2006).
Furthermore, mothers tend to report more adjustment difficulties
than do fathers across the illness trajectory (Clarke, McCarthy,
Downie, Ashley, & Anderson, 2009; Pai et al., 2007; Vrijmoet-
Wiersma et al., 2008), perhaps because mothers are often the
primary caregiver and typically accompany the child to cancer-
related procedures (Kazak et al., 1996). Given elevated risk for
depression symptoms in mothers, it is particularly important to
elucidate trajectories of mothers’ depression symptoms after their
child’s diagnosis of cancer.
Bonanno and Diminich (2013) suggest that adults’ long-term
adjustment patterns to stressful life events are most appropriately
differentiated by comparing latent trajectories of functioning in the
years after the stressor. Three trajectories have consistently
emerged across studies and types of potentially traumatic events
(e.g., natural disaster, breast cancer): (a) minimal-impact resil-
ience, characterized by a mild and transient stress response at the
time of the stressor followed by stable, low levels of distress; (b)
chronically high distress; and (c) recovery, characterized by mod-
erate to high distress that decreases to low levels (Bonanno &
Diminich, 2013). In cases of pediatric cancer, longitudinal studies
of mothers’ adjustment have generally measured mean levels of
distress, documenting elevated depression symptoms after diagno-
sis that decrease over subsequent months (Katz et al., 2018; Kazak
et al., 2015; Kearney et al., 2015). However, variance in trajecto-
ries of caregiver depression symptoms suggests the presence of
distinct latent trajectories of depression symptoms (Katz et al.,
2018). Indeed, distinct latent trajectories of parent distress have
emerged in the 6 months after diagnosis (Dolgin et al., 2007;
Steele, Dreyer, & Phipps, 2004), including high, moderate, de-
creasing, and low distress trajectories. However, it is unknown
whether these trajectories remain stable beyond 6 months. Approx-
imately one fourth of parents report clinically elevated distress
even 5 years after their child’s diagnosis (Kazak et al., 2015;
Wijnberg-Williams et al., 2006), suggesting the possibility of a
chronically distressed or delayed distress trajectory. Therefore, it is
critical to elucidate long-term trajectories of adjustment (e.g., chron-
ically high depression symptoms, resilience) and to identify risk and
resilience factors that predict membership in these trajectories.
Studies examining protective factors in parents of children with
cancer have generally focused on external (e.g., high socioeco-
nomic status, social support; Bemis et al., 2015) or trait-based
factors (e.g., personality; Kearney et al., 2015). However, internal
processes, such as perception of stress, coping, and spirituality,
have received less attention and may shed light on modifiable
protective factors that can be enhanced in interventions to promote
resilience. The diagnosis and treatment of pediatric cancer results
in marked stress for parents, including stresses of daily/role func-
tioning (e.g., job-related changes), cancer communication (e.g.,
talking to children about cancer), and cancer caregiving (e.g.,
child’s treatment-related effects; Rodriguez et al., 2012). In con-
trast to measuring external stress exposure or context-specific
types of stress (e.g., cancer-related stressors, caregiver stress),
perceived stress reflects individuals’ subjective stress reactions. In
other words, high perceived stress indicates that an individual
subjectively experiences their current life circumstances as stress-
ful. In the broader depression literature, more perceived stress has
been linked with a trajectory of chronically high depression symp-
toms, with those in a consistently low depression trajectory report-
ing less perceived stress compared to those with fluctuating de-
pression (increasing or decreasing; Repetto, Caldwell, & Zimmerman,
2004). Thus, elevated perceived stress following diagnosis might be
expected to relate to patterns of chronically elevated depression symp-
toms or recovery. Although perceived stress is broadly related to
adjustment in mothers of children with cancer (Bemis et al., 2015;
Han, 2003), it has not been examined as a predictor of mothers’
adjustment over time; thus, it is unknown whether perceived stress
following diagnosis might relate to patterns of minimal-impact resil-
ience, recovery, or more chronic distress.
Adaptive coping may serve as a resource for parents of children
with cancer (Compas et al., 2015; Compas, Jaser, Dunn, & Rodri-
guez, 2012; Maurice-Stam et al., 2008). A control-based model of
coping has been used to understand parental coping with pediatric
chronic illness and posits three voluntary coping factors: primary
control, secondary control, and disengagement, (Compas et al.,
2015; Connor-Smith, Compas, Wadsworth, Thomsen, & Saltz-
man, 2000). Primary control coping includes active, voluntary
efforts to alter the situation or a person’s emotional state (e.g.,
problem-solving, emotional regulation, emotional expression).
Secondary control coping refers to efforts to adapt to or fit into
present conditions (e.g., positive thinking, cognitive restructuring,
acceptance). Disengagement coping refers to voluntarily retreating
from addressing or acknowledging stressors (e.g., avoidance, de-
nial). Coping responses corresponding to primary or secondary
control coping are associated with fewer depression symptoms for
mothers and fathers near diagnosis (Compas et al., 2015; Maurice-
Stam et al., 2008; Turner-Sack, Menna, Setchell, Maan, &
Cataudella, 2016). In contrast, disengagement coping at diagnosis
is linked with elevated maternal distress or depression symptoms
(Greening & Stoppelbein, 2007; Lindahl Norberg, Pöder, & von
Essen, 2011). Mothers with a tendency to cope with cancer-related
stressors by disengaging might thus be expected to exhibit a
pattern of chronic depression symptoms. Longitudinal research to
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
90 HOWARD SHARP ET AL.
determine the role of coping across the cancer continuum has been
recommended (Vrijmoet-Wiersma et al., 2008); however, cancer-
specific coping has yet to be examined as a predictor of long-term
maternal depression.
Lastly, spirituality is hypothesized to serve a protective function
against the uncertainty of illness and treatment (Cadell, 2012;
Landis, 1996; Weaver & Flannelly, 2004). Separate from religion
(Hill et al., 2000), spirituality refers to existential questioning and
a search for spiritual meaning (Hatch, Burg, Naberhaus, & Hell-
mich, 1998; Hill et al., 2000). Although spirituality has not been
previously linked with depression in mothers of children with
cancer, it has been associated with depression more generally
(Koenig, 2009). Moreover, spirituality is associated with more
active coping, meaning-making, and posttraumatic growth in care-
givers of chronically ill children (Cadell, 2012; Landis, 1996;
Schneider & Mannell, 2006; Weaver & Flannelly, 2004), which
may in turn reduce the risk for depression. Thus, spirituality may
promote resilience for mothers of children newly diagnosed with
cancer, suggesting that mothers who are more spiritual may exhibit
patterns of distress more consistent with minimal-impact resilience.
This study empirically identified subgroups of mothers with
qualitatively different trajectories of depression symptoms over the
5 years after their child’s initial diagnosis/relapse using a large
sample and longitudinal prospective design. Based on contempo-
rary theory (Bonanno & Diminich, 2013) and prior pediatric can-
cer studies using smaller samples and shorter time frames (Steele
et al., 2004), it was predicted that three to six trajectories would
emerge, including patterns of chronically high, chronically low,
and decreasing depression symptoms. As a second aim, mothers’
trajectory membership was examined in relation to their perceived
stress, coping (primary control, secondary control, and disengage-
ment coping), and spirituality near the time of diagnosis. To the
extent that trajectories were characterized by chronic and/or ele-
vated depression symptoms, it was hypothesized that trajectory
membership would be predicted by more perceived stress, less
primary and secondary control coping, less spirituality, and more
disengagement coping. In contrast, it was predicted that less per-
ceived stress, more primary and secondary control coping, more
spirituality, and less disengagement coping would predict trajec-
tory membership characterized by chronically low or transient,
decreasing symptoms.
Method
Procedure
Mothers were recruited as participants in a larger, longitudinal
study examining family adjustment to childhood cancer (Compas
et al., 2015). However, the present analyses were secondary and
not a priori aims of the larger study. Following institutional review
board approval at Nationwide Children’s Hospital and Vanderbilt
University, eligible families were identified from cancer registries.
Families were eligible if their child was: (a) aged 5–17 years, (b)
recently diagnosed with new or relapsed cancer, (c) English-
speaking, and (d) without a preexisting developmental delay. El-
igible families were approached for recruitment by trained re-
search assistants in outpatient oncology clinics and inpatient rooms
at both children’s hospitals. All parents were invited to participate;
however, only one parent was required to participate for a family
to be enrolled, with the current analyses only examining the
maternal caregivers. Parents provided written informed consent.
Mothers completed questionnaires in the hospital or at home after
diagnosis/relapse (M � 2.5 months, SD � 2.0 months) and at 1 year
(M � 14.0 months postdiagnosis/relapse, SD � 3.1 month), 3 years
(M � 41.0 months postdiagnosis/relapse, SD � 3.8 months), and 5
years (M � 63.3 months postdiagnosis/relapse, SD � 5.2 months)
after enrollment. Families were compensated for their time.
Participants
Three hundred and twenty-seven mothers reported on symptoms
of depression at one or more study time point(s). Mothers of
enrolled families were eligible to participate at all time points
(even if they did not participate in baseline data collection) unless
their child died. However, mothers’ predeath data were included in
the analyses if their child had died during the course of the study
to prevent exclusion of the families of children with poor progno-
ses. At enrollment, 336 of 380 eligible families consented to
participate, and 321 mothers completed questionnaires. At approx-
imately 1-year postdiagnosis, 10\% (n � 34) of children had died,
and one child became ineligible because of a diagnosis of devel-
opmental disability. Of the remaining 301 eligible families, most
mothers had complete data at 1-year postdiagnosis (n � 217,
72\%). At the 3-year follow-up, an additional 21 children had died,
another child was ineligible because of a diagnosis of develop-
mental disability, and 47 families were not approached because
they were already beyond 3-years postdiagnosis at the time that 3-
and 5-year follow-ups were IRB approved. Of the 232 remaining
families who were approached, 46\% (n � 107) of mothers partic-
ipated. At the 5-year follow-up, four additional children had died.
Of the 275 families approached, 39\% (n � 108) of mothers
participated.
Attrition was not significantly related to primary variables of
interest in this study: depression, spirituality, coping (all p values
nonsignificant). Mothers who did not participate at 1-year postdi-
agnosis reported more perceived stress following diagnosis, t �
2.00, p � .047, had children with higher treatment intensity, t �
2.31 (204), p � .022, and were more likely to have a child who had
relapsed, �2(1) � 7.04, p � .008. However, only treatment inten-
sity significantly differentiated attrition when controlling for fam-
ilies lost to follow-up due to child death (n � 34, 10\%), t � 2.41
(200), p � .017 (other p values nonsignificant). At 3- and 5-year
follow-ups, attrition was more likely for mothers of older children,
t � 1.98 (230.42), p � .049 and t � 2.41 (322), p � .016,
respectively, and mothers of children who had relapsed, �2(1) �
17.60, p � .001 and �2(1) � 8.91, p � .003, respectively. After
controlling for child death, attrition was higher at 3-year follow-up
for children who had relapsed, �2(1) � 3.91, p � .048, and at
5-year follow-up for older children, t � 2.10 (259), p � .037. No
other demographic (age, marital status, years of education, family
income, socioeconomic status [SES], child gender) or clinical
variables (Central Nervous System [CNS] directed treatment [yes/
no], diagnosis, length of treatment) significantly differentiated
attrition (all p values nonsignificant).
Measures
Demographic and clinical variables. Mothers reported their
age, marital status, race, education, family income, number of
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
91TRAJECTORIES OF DEPRESSION SYMPTOMS
children, SES, and child gender. Children’s medical diagnosis,
date of diagnosis, date of final treatment, and treatment informa-
tion were collected through medical chart review. Length of treat-
ment was calculated by subtracting date of diagnosis from date of
final treatment. The Intensity of Treatment Rating Scale 2.0
(ITR-2) provided an overall rating of treatment intensity using
diagnosis, stage/risk level, and treatment modality (Werba et al.,
2007). Chart reviews were completed at 5-years postenrollment for
participants who had reconsented during a 3- or 5-year follow-up,
yielding incomplete length of treatment data due to missing date of
final treatment for dropouts or deceased patients.
Depression symptoms. Mothers completed the BDI-II (Beck,
Steer, & Brown, 1996), which contains 21 items ranging from 0 to
3. The total score corresponds to increasing severity of depression
symptoms, with clinical ranges of minimal (0 –13), mild (14 –19),
moderate (20 –28), and severe (29 – 63; Beck et al., 1996). This is
a well-established and standardized measure that demonstrates
good construct validity and internal consistency (Steer, Ball, Ran-
ieri, & Beck, 1997). Internal consistency in the current study was
excellent (� � .93).
Perceived stress. The Perceived Stress Scale is a 10-item
questionnaire using a Likert scale ranging from 0 (never) to 4 (very
often) that measures the extent to which one appraises events
during the past month as stressful (Cohen, Kamarck, & Mermel-
stein, 1983; Cohen & Williamson, 1988). This measure has strong
psychometric properties (Cohen et al., 1983; Cohen & Williamson,
1988) and demonstrated good internal consistency in the present
study (� � .85).
Coping. The Responses to Stress Questionnaire-Pediatric
Cancer version (RSQ-PC) is a 57-item, validated measure of
maternal coping responses to pediatric cancer (Compas et al.,
2012; Connor-Smith et al., 2000). Mothers rated how they coped
with cancer-specific stressors on a Likert scale ranging from 1 (not
at all) to 4 (a lot). The current study examined voluntary coping
factors of the RSQ-PC (i.e., primary control coping, secondary
control coping, and disengagement). Ratio scores representing
total individual factor scores divided by the total score for the
entire measure were used as recommended (Compas et al., 2015;
Connor-Smith et al., 2000). Internal consistency for these factors
was acceptable in this sample (� � .74 –.75).
Spiritual involvement and beliefs. The Spiritual Involve-
ment and Beliefs Scale (SIBS; Hatch et al., 1998) is a 24-item
questionnaire assessing engagement in spiritual actions and beliefs
without religion-specific language. Mothers rated items on a Likert
scale from 1 (strongly agree) to 5 (strongly disagree). The SIBS
consists of four subscales with strong psychometric properties,
including strong internal consistency and construct validity (Hatch
et al., 1998). However, given the high subscale intercorrelations in
the current study (r � .5–.89) and low internal consistency for the
humility/application subscale (� � .45), all items were summed
for an overall spirituality score with excellent internal consistency
(� � .91).
Statistical Analyses
Descriptive statistics and Pearson correlations were calculated
in SPSS (Version 25), with subsequent analyses conducted in
Mplus (Version 7.3). The average pattern of change in maternal
depression symptoms was first characterized using latent growth
curve modeling (LGCM). Single-group LGCMs were analyzed for
intercept-only, linear, and quadratic models and empirically com-
pared. Strong model fit is indicated by a nonsignificant �2 test
statistic, comparative fit index (CFI) � .95, root mean square error
of approximation (RMSEA) � .05, and standardized root-mean-
square residual (SRMR) � .08 (Hu & Bentler, 1999).
Homogeneous trajectories were empirically identified using la-
tent class growth analysis with maximum likelihood estimation.
Unconditional models with an increasing number of class solutions
were compared according to interpretability, theory, parsimony,
and model fit criteria (i.e., Bayesian information criteria [BIC];
Schwarz, 1978; entropy, average class assignment probabilities,
and likelihood ratio tests). Lower BIC suggests better fit, with BIC
differences of 0 –2, 2– 6, 6 –10, and �10 considered weak, posi-
tive, strong, and very strong evidence for one model over another,
respectively (Kass & Raftery, 1995; Raftery, 1995). Entropy and
average class assignment probabilities (ranging from 0 –1) reflect
classification accuracy and certainty of class assignment, respec-
tively, with larger values reflecting higher accuracy and certainty
of assignment and �.80 considered strong evidence (Rost, 2006).
Lastly, the Lo-Mendell-Rubin test (LMR; Lo, Mendell, & Rubin,
2001) and the bootstrap likelihood ratio difference test (BLRT;
McLachlan & Peel, 2000) compare each model with the model
containing one fewer class (e.g., four-class solution vs. the three-
class solution). Models were estimated using all available data and
missing data were estimated using maximum likelihood, which is
suitable for small sample sizes and yields similar to less biased
estimates compared to multiple imputation (Shin et al., 2017).
The three-step approach was used (Asparouhov & Muthén,
2014; Vermunt, 2010) to examine how perceived stress, coping,
and spirituality at diagnosis related to mothers’ membership in the
identified latent trajectories. This approach conducts a multinomial
logistic regression predicting latent class membership while main-
taining the probabilistic nature of the latent class variable and
accounting for the variance of all predictors. Therefore, rather than
assigning participants to their most likely class, participants re-
tained partial membership in classes according to their probability
of class membership. Given that maternal age, education, income,
race, relapse, and child death are associated with maternal depres-
sion in this population (Bemis et al., 2015; Wijnberg-Williams et
al., 2006), these variables were included as covariates (i.e., 1 �
relapse before/during study, 0 � never relapsed; 1 � child de-
ceased, 0 � child living) in the model with significant predictors.
Treatment intensity was also included as a covariate given that it
significantly differentiated attrition at 1-year follow-up. Missing
data for these analyses were estimated using multiple imputation
given that Mplus Version 7.3 does not permit estimation of miss-
ing data using maximum likelihood for the three-step approach.
Results
Preliminary Analyses
Demographic information for patients and mothers is presented
in Table 1. Means, standard deviations, and correlations are pre-
sented in Table 2. On average, mothers’ BDI-II scores were in the
mild range (Beck et al., 1996) at diagnosis (M � 15.00, SD �
10.59) and in the minimal range 1 to 5 years after study enrollment
(M � 8.61–12.27, SD � 8.74 –10.95). At diagnosis/relapse, 11\%
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.
92 HOWARD SHARP ET AL.
(n � 35) of mothers reported BDI-II scores in the severe range,
and 18\% (n � 57) reported scores in the moderate range. At 1-year
follow-up, 7\% (n � 16) reported scores in the severe range, and
16\% (n � 35) reported scores in the moderate range. At 3-years
postdiagnosis/relapse, 5\% (n � 5) reported scores in the severe
range, and 10\% (n � 11) reported scores in the moderate range. At
5-years postdiagnosis/relapse, 6\% (n � 6) reported scores in the
severe range, and 3\% (n � 3) reported scores in the moderate
range.
Depression symptoms were negatively correlated with primary
and secondary control coping and positively correlated with dis-
engagement coping and perceived stress (see Table 2). Spirituality
was significantly correlated with depression symptoms at diagno-
sis/relapse and 1 year, but not at 3 or 5 years. Coping variables
were associated with perceived stress and spirituality in expected
directions, and coping factors were highly intercorrelated. Toler-
ance (all �.47) and the variance inflation factor (VIF; all �2.12)
suggest minimal multicollinearity despite …
CATEGORIES
Economics
Nursing
Applied Sciences
Psychology
Science
Management
Computer Science
Human Resource Management
Accounting
Information Systems
English
Anatomy
Operations Management
Sociology
Literature
Education
Business & Finance
Marketing
Engineering
Statistics
Biology
Political Science
Reading
History
Financial markets
Philosophy
Mathematics
Law
Criminal
Architecture and Design
Government
Social Science
World history
Chemistry
Humanities
Business Finance
Writing
Programming
Telecommunications Engineering
Geography
Physics
Spanish
ach
e. Embedded Entrepreneurship
f. Three Social Entrepreneurship Models
g. Social-Founder Identity
h. Micros-enterprise Development
Outcomes
Subset 2. Indigenous Entrepreneurship Approaches (Outside of Canada)
a. Indigenous Australian Entrepreneurs Exami
Calculus
(people influence of
others) processes that you perceived occurs in this specific Institution Select one of the forms of stratification highlighted (focus on inter the intersectionalities
of these three) to reflect and analyze the potential ways these (
American history
Pharmacology
Ancient history
. Also
Numerical analysis
Environmental science
Electrical Engineering
Precalculus
Physiology
Civil Engineering
Electronic Engineering
ness Horizons
Algebra
Geology
Physical chemistry
nt
When considering both O
lassrooms
Civil
Probability
ions
Identify a specific consumer product that you or your family have used for quite some time. This might be a branded smartphone (if you have used several versions over the years)
or the court to consider in its deliberations. Locard’s exchange principle argues that during the commission of a crime
Chemical Engineering
Ecology
aragraphs (meaning 25 sentences or more). Your assignment may be more than 5 paragraphs but not less.
INSTRUCTIONS:
To access the FNU Online Library for journals and articles you can go the FNU library link here:
https://www.fnu.edu/library/
In order to
n that draws upon the theoretical reading to explain and contextualize the design choices. Be sure to directly quote or paraphrase the reading
ce to the vaccine. Your campaign must educate and inform the audience on the benefits but also create for safe and open dialogue. A key metric of your campaign will be the direct increase in numbers.
Key outcomes: The approach that you take must be clear
Mechanical Engineering
Organic chemistry
Geometry
nment
Topic
You will need to pick one topic for your project (5 pts)
Literature search
You will need to perform a literature search for your topic
Geophysics
you been involved with a company doing a redesign of business processes
Communication on Customer Relations. Discuss how two-way communication on social media channels impacts businesses both positively and negatively. Provide any personal examples from your experience
od pressure and hypertension via a community-wide intervention that targets the problem across the lifespan (i.e. includes all ages).
Develop a community-wide intervention to reduce elevated blood pressure and hypertension in the State of Alabama that in
in body of the report
Conclusions
References (8 References Minimum)
*** Words count = 2000 words.
*** In-Text Citations and References using Harvard style.
*** In Task section I’ve chose (Economic issues in overseas contracting)"
Electromagnetism
w or quality improvement; it was just all part of good nursing care. The goal for quality improvement is to monitor patient outcomes using statistics for comparison to standards of care for different diseases
e a 1 to 2 slide Microsoft PowerPoint presentation on the different models of case management. Include speaker notes... .....Describe three different models of case management.
visual representations of information. They can include numbers
SSAY
ame workbook for all 3 milestones. You do not need to download a new copy for Milestones 2 or 3. When you submit Milestone 3
pages):
Provide a description of an existing intervention in Canada
making the appropriate buying decisions in an ethical and professional manner.
Topic: Purchasing and Technology
You read about blockchain ledger technology. Now do some additional research out on the Internet and share your URL with the rest of the class
be aware of which features their competitors are opting to include so the product development teams can design similar or enhanced features to attract more of the market. The more unique
low (The Top Health Industry Trends to Watch in 2015) to assist you with this discussion.
https://youtu.be/fRym_jyuBc0
Next year the $2.8 trillion U.S. healthcare industry will finally begin to look and feel more like the rest of the business wo
evidence-based primary care curriculum. Throughout your nurse practitioner program
Vignette
Understanding Gender Fluidity
Providing Inclusive Quality Care
Affirming Clinical Encounters
Conclusion
References
Nurse Practitioner Knowledge
Mechanics
and word limit is unit as a guide only.
The assessment may be re-attempted on two further occasions (maximum three attempts in total). All assessments must be resubmitted 3 days within receiving your unsatisfactory grade. You must clearly indicate “Re-su
Trigonometry
Article writing
Other
5. June 29
After the components sending to the manufacturing house
1. In 1972 the Furman v. Georgia case resulted in a decision that would put action into motion. Furman was originally sentenced to death because of a murder he committed in Georgia but the court debated whether or not this was a violation of his 8th amend
One of the first conflicts that would need to be investigated would be whether the human service professional followed the responsibility to client ethical standard. While developing a relationship with client it is important to clarify that if danger or
Ethical behavior is a critical topic in the workplace because the impact of it can make or break a business
No matter which type of health care organization
With a direct sale
During the pandemic
Computers are being used to monitor the spread of outbreaks in different areas of the world and with this record
3. Furman v. Georgia is a U.S Supreme Court case that resolves around the Eighth Amendments ban on cruel and unsual punishment in death penalty cases. The Furman v. Georgia case was based on Furman being convicted of murder in Georgia. Furman was caught i
One major ethical conflict that may arise in my investigation is the Responsibility to Client in both Standard 3 and Standard 4 of the Ethical Standards for Human Service Professionals (2015). Making sure we do not disclose information without consent ev
4. Identify two examples of real world problems that you have observed in your personal
Summary & Evaluation: Reference & 188. Academic Search Ultimate
Ethics
We can mention at least one example of how the violation of ethical standards can be prevented. Many organizations promote ethical self-regulation by creating moral codes to help direct their business activities
*DDB is used for the first three years
For example
The inbound logistics for William Instrument refer to purchase components from various electronic firms. During the purchase process William need to consider the quality and price of the components. In this case
4. A U.S. Supreme Court case known as Furman v. Georgia (1972) is a landmark case that involved Eighth Amendment’s ban of unusual and cruel punishment in death penalty cases (Furman v. Georgia (1972)
With covid coming into place
In my opinion
with
Not necessarily all home buyers are the same! When you choose to work with we buy ugly houses Baltimore & nationwide USA
The ability to view ourselves from an unbiased perspective allows us to critically assess our personal strengths and weaknesses. This is an important step in the process of finding the right resources for our personal learning style. Ego and pride can be
· By Day 1 of this week
While you must form your answers to the questions below from our assigned reading material
CliftonLarsonAllen LLP (2013)
5 The family dynamic is awkward at first since the most outgoing and straight forward person in the family in Linda
Urien
The most important benefit of my statistical analysis would be the accuracy with which I interpret the data. The greatest obstacle
From a similar but larger point of view
4 In order to get the entire family to come back for another session I would suggest coming in on a day the restaurant is not open
When seeking to identify a patient’s health condition
After viewing the you tube videos on prayer
Your paper must be at least two pages in length (not counting the title and reference pages)
The word assimilate is negative to me. I believe everyone should learn about a country that they are going to live in. It doesnt mean that they have to believe that everything in America is better than where they came from. It means that they care enough
Data collection
Single Subject Chris is a social worker in a geriatric case management program located in a midsize Northeastern town. She has an MSW and is part of a team of case managers that likes to continuously improve on its practice. The team is currently using an
I would start off with Linda on repeating her options for the child and going over what she is feeling with each option. I would want to find out what she is afraid of. I would avoid asking her any “why” questions because I want her to be in the here an
Summarize the advantages and disadvantages of using an Internet site as means of collecting data for psychological research (Comp 2.1) 25.0\% Summarization of the advantages and disadvantages of using an Internet site as means of collecting data for psych
Identify the type of research used in a chosen study
Compose a 1
Optics
effect relationship becomes more difficult—as the researcher cannot enact total control of another person even in an experimental environment. Social workers serve clients in highly complex real-world environments. Clients often implement recommended inte
I think knowing more about you will allow you to be able to choose the right resources
Be 4 pages in length
soft MB-920 dumps review and documentation and high-quality listing pdf MB-920 braindumps also recommended and approved by Microsoft experts. The practical test
g
One thing you will need to do in college is learn how to find and use references. References support your ideas. College-level work must be supported by research. You are expected to do that for this paper. You will research
Elaborate on any potential confounds or ethical concerns while participating in the psychological study 20.0\% Elaboration on any potential confounds or ethical concerns while participating in the psychological study is missing. Elaboration on any potenti
3 The first thing I would do in the family’s first session is develop a genogram of the family to get an idea of all the individuals who play a major role in Linda’s life. After establishing where each member is in relation to the family
A Health in All Policies approach
Note: The requirements outlined below correspond to the grading criteria in the scoring guide. At a minimum
Chen
Read Connecting Communities and Complexity: A Case Study in Creating the Conditions for Transformational Change
Read Reflections on Cultural Humility
Read A Basic Guide to ABCD Community Organizing
Use the bolded black section and sub-section titles below to organize your paper. For each section
Losinski forwarded the article on a priority basis to Mary Scott
Losinksi wanted details on use of the ED at CGH. He asked the administrative resident