Close
About
FAQ
Home
Collections
Login
USC Login
Register
0
Selected
Invert selection
Deselect all
Deselect all
Click here to refresh results
Click here to refresh results
USC
/
Digital Library
/
University of Southern California Dissertations and Theses
/
Low-income, minority cancer patients who drop out of depression treatment
(USC Thesis Other)
Low-income, minority cancer patients who drop out of depression treatment
PDF
Download
Share
Open document
Flip pages
Contact Us
Contact Us
Copy asset link
Request this asset
Transcript (if available)
Content
LOW-INCOME, MINORITY CANCER PATIENTS WHO DROP OUT OF DEPRESSION
TREATMENT
by
Anjanette A. Wells
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(SOCIAL WORK)
May 2009
Copyright 2009 Anjanette A. Wells
ii
Dedication
I passionately dedicate this dissertation to my husband, our children, and my
parents. Julius, you have been my rock throughout this journey, from the start of my
MSW, to the completion of my PhD. I love you! Julius (III), Mikayla, and Mya, you have
been my most enthusiastic fans; I hope this accomplishment inspires you to reach,
achieve, and succeed in every dream you set forth in your lives. Mom and Daddy, you
are my role models. Your endless sacrifices, guidance, and loving support throughout
my life have been instrumental in attaining my educational goals. You both have instilled
confidence in my abilities, supported and encouraged me throughout my education,
even when I lacked self-belief. And Lemar, you have not only been a loving father, but
also a special friend who I could always call on 24-7; your unerring emotional and
intellectual support and guidance through the ups and downs of this doctoral process
and the writing of this dissertation were critical to my completion and graduation.
iii
Acknowledgements
I need to first acknowledge my loving and ever-supportive family: Julius, Julius (III),
Mikayla, Mya, Mom, Daddy, and Lemar; thank you for your infinite patience and never-
ending support.
I would like to acknowledge my A+ dissertation committee members, all whom were
influential to my doctoral education: Larry Palinkas, Ph.D., who believed in me and has
been fundamental to my progress, success, and completion of my Ph.D.; Kathleen Ell,
D.S.W., who willingly offered numerous research and training opportunities and support,
even prior to my beginning the Ph.D. program; and Tess Cruz, Ph.D., my health
behavior theory guru, whose patience, support, and guidance were instrumental to my
completion of my dissertation. I also need to thank my informal mentors who have
uniquely contributed to various aspects of my doctoral education and job search:
Concepcion Barrio, Ph.D., John Brekke, Ph.D., Devon Brooks, Ph.D., Bruce Jansson,
Ph.D., Karen Lincoln, Ph.D., Lorenzo Merritt, Ph.D., Tyan Parker-Dominguez, Ph.D., and
Brad Zebrack, Ph.D.
I need to acknowledge the American Cancer Society for a doctoral oncology
fellowship, and the National Cancer Institute for a Research Supplement and a F31
dissertation fellowship. These organizations have funded my research studies and
doctoral education, by way of phenomenal financial resources, research training and
mentored support. I also cannot forget a few other key people who helped with
challenging recruitment and data collection efforts for this study: Sylvia Barker, Maria
Hu-Cordova, M.S.W., and Yvonne Parades-Alexander, M.P.H. A special thank you to
“dropout” and “completer” participants for sharing your insightful stories.
iv
Table of Contents
Dedication ....................................................................................................................... ii
Acknowledgments .......................................................................................................... iii
List of Tables ................................................................................................................. vii
List of Figures ............................................................................................................... viii
Abstract .......................................................................................................................... ix
Chapter 1: The Problem and Its Underlying Framework .................................................. 1
Background of the Problem .......................................................................................... 1
Purpose of the Study.................................................................................................... 2
Specific Aims ............................................................................................................... 3
Significance of the Problem.......................................................................................... 3
Methodology ................................................................................................................ 4
Organization of the Study ............................................................................................. 6
Chapter 2: Review of the Literature ................................................................................. 7
Depression ................................................................................................................... 8
Depression along the Cancer Continuum ................................................................. 8
Major Depression and Depressive Disorder ............................................................. 9
Prevalence ............................................................................................................. 10
Prevalence among Low Income Minority Groups ................................................... 11
Quality of Life among Depressed Cancer Patients ................................................. 13
Immune Response among Depressed Cancer Patients ......................................... 14
Morbidity and Depressed Cancer Patients ............................................................. 15
Adherence to Medical Treatment and Depressed Cancer Patients ......................... 15
Mortality and Depressed Cancer Patients .............................................................. 16
Depression Treatment ................................................................................................ 16
Depression Treatment among Ethnic Minority Populations ..................................... 18
Depression Treatment Non-Adherence ...................................................................... 20
Depression Treatment Non-Adherence and Ethnic Minority Populations ................ 24
Factors Affecting Retention to Depression Treatment ............................................ 27
Correlated Characteristics of the Patient ................................................................ 29
Correlated Characteristics of the Treatment Regimen ............................................ 32
Correlated Features of the Disease ........................................................................ 33
Depression Treatment Completers ......................................................................... 34
Provider Retention Strategies .................................................................................... 35
Theoretical Foundations ............................................................................................. 38
Chapter 3: Research Methodology ................................................................................ 45
Research Aims........................................................................................................... 45
ADAPt-C Depression Treatment Model (Parent Grant) .............................................. 47
Patient Inclusion/Exclusion Criteria ............................................................................ 50
Sampling Plan ............................................................................................................ 51
Sampling Plan- Phases 1 and 2 ............................................................................. 52
v
Sampling Plan- Phase 3 ......................................................................................... 53
Sampling Plan- Phase 4 ......................................................................................... 54
Data Collection........................................................................................................... 55
Data Collection- Phases 1 and 2 ............................................................................ 55
Data Collection- Phase 3 ........................................................................................ 62
Data Collection- Phase 4 ........................................................................................ 63
Data Analysis ............................................................................................................. 63
Data Analysis- Phases 1 and 2............................................................................... 63
Data Analysis- Phase 3 .......................................................................................... 65
Data Analysis- Phase 4 .......................................................................................... 66
Products from 4 Phases of Data Analysis .................................................................. 67
Protection of Human Subjects .................................................................................... 68
Anticipated Benefits Relative to Risks .................................................................... 69
Procedures for Protecting Against Potential Risks .................................................. 70
Inclusion of Women, Minorities, and Children ......................................................... 70
Data and Safety Monitoring Plan ............................................................................ 70
Chapter 4: Sample Descriptions .................................................................................... 71
Phase 1 and 2 Recruitment Details ............................................................................ 71
Interview Details and Subtleties .............................................................................. 72
Description of Phases 1 and 2 Samples ..................................................................... 75
Phase 3 Recruitment Details ...................................................................................... 76
Phase 4 Description of the Providers ......................................................................... 79
Chapter 5: Results ......................................................................................................... 80
Aim 1: Patients’ Perspectives on Their Reasons for Dropping Out and Barriers to
Treatment Adherence ................................................................................................ 80
Patient Definition and Meaning of “Dropout” ........................................................... 81
Dropout Barriers to Completion and Reasons ....................................................... 82
Cancer-related Barriers ......................................................................................... 84
Depression Treatment Barriers .............................................................................. 85
Informational Barriers ............................................................................................ 86
Instrumental Barriers ............................................................................................. 87
Cultural Barriers .................................................................................................... 91
Systems’ Barriers .................................................................................................. 94
Multiple Confounding Barriers ............................................................................... 97
Additional Concepts to Explore ............................................................................ 100
Aim 2: Completers’ Perspectives, Barriers, and Enablers to Depression Treatment
Adherence ............................................................................................................... 107
Barrier Comparison .............................................................................................. 107
Completer-Dropout Enabler Supports Comparison .............................................. 109
Clinically Desired Enablers .................................................................................. 110
Aim 3: Provider Clinical Notes Offer Additional Barriers to Treatment Completion ... 113
Aim 4: Provider Strategies and Perspectives on Decreasing Patient Dropout and
Increasing Retention ................................................................................................ 117
Rapport Building as a Key Retention Strategy ...................................................... 128
Outcome-focused Retention Strategies ............................................................... 129
Patient Education Strategies in Addressing Informational Barriers ....................... 131
Concrete Patient Navigation and Case Management Strategies .......................... 133
Patient and Provider Systems Strategies ............................................................ 135
vi
Chapter 6: Discussion ................................................................................................. 137
Summary of Aims ..................................................................................................... 137
Summary of Findings ............................................................................................... 137
Theoretical Implications ........................................................................................... 140
Research Design Implications .................................................................................. 142
Sample Characteristics ........................................................................................ 143
Recruitment Dynamics ........................................................................................ 143
Considering Incentives ......................................................................................... 145
Interview Subtleties .............................................................................................. 145
Maintaining Contact ............................................................................................. 147
Clinical Social Work Implications .............................................................................. 147
Study Limitations ...................................................................................................... 156
Future Community-Based Research Implications ..................................................... 157
References .................................................................................................................. 160
Appendices ................................................................................................................. 177
Appendix 1: Telephone Interview Debriefing/Feedback Form-English ...................... 177
Appendix 2: Telephone Interview Debriefing/Feedback Form-Spanish .................... 178
Appendix 3: Provider Interview Protocol ................................................................... 179
vii
List of Tables
Table 1: Origination of Dropout Interview Script Prompt Examples and Exploration
Areas ......................................................................................................................... 57
Table 2: Dropout/Completer Demographics, Depression, and Characteristics ........... 76
Table 3: Multiple Confounding Barriers ...................................................................... 98
Table 4: Comparison of Barriers to Depression Treatment ....................................... 108
Table 5: Comparison of Enablers to Depression Treatment ..................................... 110
Table 6: Provider Strategies ..................................................................................... 119
viii
List of Figures
Figure 1: Dropout Recruitment Details ....................................................................... 72
Figure 2: Phase 2 Medical Release Form Recruitment Details ................................... 78
Figure 3: Dropout Barriers and Reasons .................................................................... 83
Figure 4: Provider Explanatory Model of Dropout Patient Barriers............................ 116
ix
Abstract
Depression is one of the most common symptoms of cancer, having a profound
impact on patients’ quality of life, immune response, morbidity, adherence to treatment,
and even mortality. Although medication and counseling are effective in reducing
depressive symptoms in cancer patients, there is an increasing need to understand
factors that contribute to dropout (and retention) of low-income, minority cancer patients
to depression treatment.
The aim of this study was to explore and understand the barriers and enablers
contributing to low-income minority cancer survivors’ participation and completion of
depression treatment, within the context of an effectiveness treatment trial (ADAPt-C
study). Such perspectives were gained through in-depth, telephone interviews with 20
patients who had dropped out of treatment, compared to 10 similar patients’ who had
completed depression treatment. Additional understanding about the dropout barriers
were gained through secondary analysis of provider clinical notes. Face-to-face and
telephone interviews with providers offered a list of strategies to retain patients to
treatment.
Findings revealed that patients who dropped out of treatment perceived and
described key barriers which interfered with their treatment completion: cancer-related,
depression treatment-related, informational, instrumental, cultural, and systems-related.
Taken together, dropouts often described “multiple confounding barriers.” Completers
experienced equally as many “multiple confounding barriers”, however were able to
continue and complete treatment. Potential explanations for this discrepancy can be
found within some of the discrete barriers and associated narratives themselves.
Analyses of dropout clinical notes from providers help reveal that additional barriers
contributed to dropout: Enabling Family factors and Additional Enabling Psychological
x
Coping factors. Provider interviews indicated that providers can provide feasible
strategies which address some of the various patient-identified barriers: 1) Depression
treatment strategies; 2) Informational strategies, 3) Instrumental strategies; 4)
Recruitment strategies; 5) Cultural strategies; and 6) Systems’ strategies. These results
point to implications in the following areas: theoretical, research study design, clinical
social work, community-based, and further research in the areas of health behavior
decision-making, the influence of motivation, and self-efficacy. This adherence study of
low-income, minority depressed cancer patients is especially important to future
research and real-world depression care among cancer patients, particularly those from
hard-to-reach populations.
Chapter 1: The Problem and It’s Underlying Framework
Background of the Problem
According to the National Institutes of Health State-of-the-Science Conference on
Symptom Management in Cancer: Pain, Depression and Fatigue (2002), depression
was identified as one of the most common symptoms of cancer, having a profound
impact on patients’ quality of life, immune response, morbidity, adherence to treatment,
and even mortality. Although about half of cancer patients are adjusting normally to the
stressors of cancer with no diagnosable psychiatric disorder (Derogatis et al., 1983),
58% of cancer patients still have depressive symptoms and between 38% (Cella et al.,
1993) and 50% have major depression
(Massie, 2004), which varies by cancer site and
assessment method
(National Institute of Health State of the Science Panel, 2004). In
response, a number of trials show that psychotherapeutic interventions (especially
cognitive behavioral therapy) have favorable effects on depressive symptoms in cancer
patients
(Williams & Dale, 2006). Additionally, when psychotherapy is combined with
antidepressant treatment, together they have direct effects on response to
chemotherapy, treatment adherence, and quality of life for cancer patients (Dwight-
Johnson et al., 2005; Khouzam et al., 1998; McDonough et al., 1996; Walker et al.,
1999).
Although, psychotherapeutic and antidepressant medication interventions are
effective for depression among cancer patients (National Institute of Health State of the
Science Panel, 2004), depression often remains undetected and untreated in cancer
patients (McDaniel, Musselman, Porter, Reed, & Nemeroff, 1995; Williams & Dale,
2006), especially in minority and hard-to-reach populations. Furthermore, patients with
cancer who are most in need of psychosocial interventions (Rawl et al., 2002), may be
more likely to drop out (National Institute of Health State of the Science Panel, 2004;
2
Rawl et al., 2002) of psychotherapy and/or medication treatment, with psychological
support seldom being assessed (Moynihan, 1998) and the inability to identify (Raison &
Miller, 2003) and customize effective interventions. For example, in a systematic review
of treatment for depression and depressive symptoms in adults with cancer, dropout
rates for intervention and controlled trials ranged up to 41% (Williams & Dale, 2006).
Importantly, depression is a factor in 50% of all suicides (Breitbart, 1995), and research
shows that cancer patients almost universally, reveal that they have occasionally had
persistent thoughts of suicide as a means of escaping the threat of being overwhelmed
by cancer (Breitbart, 1995). Literature also suggests cultural and socio-demographic
factors are involved in the decision to drop out of treatment. Thus, depression treatment
dropout is a major problem for delivering effective mental health care (Johansson &
Eklund, 2006; Thormahlen, 2003), which represents a missed opportunity to reduce
psychological distress and facilitate a client’s engagement in treatment (Reece, 2003).
Purpose of the Study
Although there has been increasingly more precision in measuring the
prevalence of depression among cancer patients, previous studies have failed to
distinguish between caseness for depression and depressive symptoms (Williams &
Dale, 2006). This complicates attempts at research with depressed cancer patients and
creates completion barriers. Furthermore, less is known about non-adherence and
dropout among low-income, minority depressed cancer patients. Existing psychosocial
intervention studies have either been based almost exclusively on White populations
(Miranda, Chung, Green, & et al, 2003; Raison & Miller, 2003), fail to describe the ethnic
composition of the sample, completely omit discussion of ethnic minority sample
description, were not conducted in public care systems, or provide little explanation of
reasons for dropout (Davis, Evans, Fishman, Haley, & Spielman, 2004; Ogrodniczuk,
3
Joyce, & Piper, 2005; Thormahlen, 2003). In-depth patient perspectives are critical
because such patients may fail to engage in and accept treatment for a variety of
reasons rooted in their own cultural background and personal experience (Montano &
Kasprzyk, 2002), which are important to understand in order to develop and customize
strategies for addressing the problem.
Specific Aims
This study aimed to identify factors associated with depression treatment dropout
among low-income minority (predominately Latino) cancer patients and strategies that
providers can use to help decrease dropout in a public care system. Specifically, this
study involved four sequential phases of research:
1. Explore low-income, minority cancer patient perspectives about depression
treatment, their reasons for dropping out of treatment, and barriers to treatment
adherence;
2. Compare these dropout perspectives and barriers to low-income, minority cancer
patients who complete depression treatment, while also eliciting their enablers of
treatment;
3. Understand the barriers contributing to dropout, from providers’ perspectives;
and
4. Identify viable study provider strategies to decrease dropout and increase
retention to treatment.
Significance of the Problem
This study was not only important for cancer care and oncology practice with
vulnerable, hard-to-reach populations, but will also laid the groundwork for future
community-based engagement and retention intervention research with low-income,
minority depressed cancer patients and populations that experience chronic illness.
4
Methodology
Sampling from dropout participants from an NCI-funded investigation of
depression treatment of cancer patients - Alleviating Depression among Patients with
Cancer (ADAPt-C) (R01 CA105269) (Ell et al., 2007), this study used a grounded theory
(Glaser & Strauss, 1967) qualitative methodological approach throughout to explore and
generate new theory (Creswell & Maietta, 2002) on depression treatment dropout for
cancer patients. Using sensitizing concepts from the literature and theoretical
frameworks (e.g., Andersen and Newman’s [1973, 2005] Individual and Social
Determinants of Health Service Utilization, TPB (Fishbein, 1967; Fishbein & Ajzen,
1975), and Cultural Explanatory Theory (Kleinmann, Eisenberg, & Good, 1978), this
study involved 4 sequential methodological strategies associated with each aim:
1) Twenty individual dropout patient telephone interviews were used to explore
barriers to depression treatment adherence and completion, and their
perspectives about depression treatment;
2) Ten individual completer patient telephone interviews were used to compare
dropout barriers and enablers, to those who complete depression treatment;
3) Secondary data analysis of clinical depression provider clinical notes were used to
understand the barriers leading up to dropout, from the provider’s perspective;
and
4) Face-to-face and telephone interviews with parent study providers (social work
clinicians, psychiatrist, study recruiters, project assistant, etc.) were used to
identify strategies to decrease dropout.
The strength of this qualitative study was the use of 3 types of triangulation
throughout, to enhance the overall rigor and credibility of findings: 1) data triangulation
(the use of a variety of data sources); 2) investigator triangulation (the use of multiple
5
interviewers); and 3) theory triangulation (the use of multiple perspectives to interpret a
single set of data) (Denzin, 1978; Patton, 2002). Socio-cultural, psychosocial, provider,
and health system factors’ influence on dropout were explored using individual patient
interviews, secondary data analysis of provider notes, and provider interviews. These
inductive methods of data collection were expected to elicit patient and provider
perspectives on depression, the utility of depression treatment, barriers and enablers to
completing treatment, reasons for dropping out of treatment, and viable provider
strategies for reducing dropout. Such exploration was expected to generate further
related discussion about the experience of depression and cancer, help-seeking
behaviors, treatment experience, stigma, cancer and cancer treatment self-
management, family support and other culturally-mediated coping resources such as
religion, and other multi-level barriers and facilitative factors to cancer and depression
(Campbell et al., 2003).
The research activities took as their starting point, conceptual and theoretical
models of health behavior, which utilize psychosocial, socio-demographic, and cultural
factors to explain influence on health behavior decisions, particularly to dropout of
depression treatment. However, rather than test these theories, this study intended to
build a new explanatory model of theory through the generation of a heuristic model of
patient decision-making with respect to depression treatment and dropout. Sensitizing
concepts (Blumer, 1954) were incorporated into the individual interview guides, which
focused on basic descriptions of a dropout’s intention for depression treatment, through
the TPB’s subjective norm, attitude, and perceived behavioral control theoretical
constructs. This was not only expected to yield a better understanding about patient
barriers, enablers, and strategies, but was also was expected to gain knowledge about
depression and depression treatment perspectives, and the role of support. The goal
6
was to elicit each patients’ explanatory model (Kleinmann et al., 1978) of the cause
(etiology) course (symptomatology) and cure (appropriate response or treatment) of
depression to determine its origins as well as the role in the decision to withdraw from
depression treatment, within the context of a treatment trial.
Organization of the Study
Chapter 1 presents the background of the problem under study, the purpose of the
study, the specific aims to be addressed, the significance of the problem, the
methodology to be used, and the definition of terms.
Chapter 2 is a review of the relevant literature. It addresses the following topics:
depression, depression treatment non-adherence, correlates of dropout, and cultural
influences on depression treatment dropout.
Chapter 3 presents the methodology used in the study, including the research aims, the
research design, human subjects’ protections, the sampling procedures, data collection
methods, including the qualitative plan for analysis and interpretation.
Chapter 4 presents overall descriptions of samples used in the qualitative analysis,
including descriptive information about the sample of dropouts and completers, interview
subtleties, and recruitment details.
Chapter 5 presents the qualitative findings of the study from the grounded theory of
semi-structured interviews with dropout and completer participants, the secondary
analysis of provider clinical notes of dropout patients, and provider interviews.
Chapter 6 presents discussion, interpretation, implication of the findings, study
limitations, and future directions.
7
Chapter 2: Literature Review
Until recently, the psychological and social impact of a cancer diagnosis had
been relatively neglected and unrecognized as a critical aspect of clinical oncology
(Akechi et al., 2006). However, over the past decade there has been increasing attention
to the importance of the psychosocial costs of cancer as cancer technologies have
developed and patients have become more willing to accept higher risks, trading off
potential deficits, for a chance to live longer lives (Sivesind & Baile, 2001). In 2002, the
National Institutes of Health State-of-the-Science Conference on Symptom Management
in Cancer recognized depression (in addition to pain and fatigue) as one of the most
common, yet understudied symptoms of cancer (National Institute of Health State of the
Science Panel, 2004). In 2007, the Institute of Medicine (IOM) called for prompt attention
and intervention to address the profound psychosocial needs of cancer patients (2008).
Depression among cancer patients has been shown to have negative effects on
quality of life, immune response, morbidity, adherence to medical treatment, and even
mortality, particularly as disease advances and as cancer treatments become more
aggressive (Breitbart, 1995). There is also growing evidence that psychotherapy and
antidepressant medication interventions are effective for reducing depression among
cancer patients (National Institute of Mental Health, 2002). However, those in most need
are not receiving adequate treatment. Depression often remains undetected and
untreated in cancer patients, especially in minority, low-income, and hard-to-reach
populations (McDaniel et al., 1995; Rawl, 2002; Williams & Dale, 2006). Furthermore,
patients with cancer who are most in need of psychosocial interventions (Rawl, 2002),
may be more likely to drop out of psychotherapy and/or medication treatment (Moynihan,
1998; Raison & Miller, 2003), due primarily to patient and cancer treatment regimens
and features of their cancer. Unfortunately, the literature only provides limited empirical
8
and integrated strategies which can be used to retain these patients to depression
treatment. Additionally, the literature lacks theoretically meaningful health behavior
models that are relevant to non-adherence and dropout among cancer patients,
particularly minority and low-income groups.
This literature review addresses a population, low-income, minority
(predominately Latina) depressed cancer patients, who have been historically
unrecognized in the clinical oncology literature. The purpose of this literature review is
to: 1) identify factors affecting retention to depression treatment (in the context of a
clinical trial or real world practice) among both cancer patients in general and specifically
with minority populations; and 2) identity congruent strategies to address the barriers to
treatment. These objectives will be addressed while simultaneously underscoring the
gaps in the current knowledge base.
Depression
Depression along the Cancer Continuum
Despite recent national attention to depression and the psychosocial needs of
cancer patients, the field of clinical oncology is just starting to seriously recognize and
understand the importance of depression and depression treatment throughout the
various phases of the cancer care continuum. The single most important factor
precluding treatment among depressed cancer patients is the misconception that for
such patients being depressed is normal (Pasquini & Biondi, 2007). However, it has
been shown that the psychosocial impact of depression along the cancer continuum has
important implications for cancer care and future research. Cancer is a risk factor for
developing clinical depression and experiencing depressive symptoms (Barsevick,
Sweeney, Haney, & Chung, 2002) at the initial phase of diagnosis, during treatment, or
over a long period of time as they adjust to life changes (Sivesind & Baile, 2001).
9
Importantly, it has also been shown that not only does cancer create emotional
problems, like depression and distress, but it can be a factor in causing disease
(Auslander & Freedenthal, 2006).
During the first year following the diagnosis of cancer, there will often be a
process of emotional adaptation, involving adjustment to uncertainty, anxiety, and loss or
changes in bodily functions and roles (Marchioro et al., 1996; Wells & Turney, 2001). An
initial diagnosis is compounded when the patient immediately faces a number of critical
decisions (e.g. types of treatment, lifestyle changes) for which they have little training or
preparation (O'Hair et al., 2003). The early assessment of depression is usual during the
course of the disease, when cancer patients experience several stressors and emotional
upheavals (e.g. the fear of death, interruption of life plans, changes in body image and
self-esteem, changes in social role and lifestyle) (Hopko, Bell, Armento, Hunt, & Lejuez,
2005; Pasquini & Biondi, 2007). In addition to the emotional problems, there are also
social and economic costs of a cancer diagnosis (Stokes, 1993). And for advanced
cancer patients, clinical depression and related psychosocial problems have been
considered the most distressing effects of the disease (Okamura et al., 2008). Thus, the
timing of determining whether depressive symptoms are a normal adjustment of
depression versus major depression is important to distinguish in assessing depression
levels.
Major Depression and Depressive Disorder
Studies show that both major depressive disorder and depressive symptoms are
common among patients with a variety of cancers (Archer, Hutchison, & Korszun, 2008;
Davis et al., 2004; Ersoy, Noyan, & Elbi, 2008; Hopko et al., 2005; Marchioro et al.,
1996; Meyerowitz, Formenti, Ell, & Leedham, 2000; Schneider & Chiriboga, 2005).
Although some studies differentiate between depressive symptoms and a major
10
depressive disorder (Archer et al., 2008; Ell, Wells, Nedjat-Haiem, Lee, & Vourlekis,
2008), cancer and depression studies often use the general term “depression”, to denote
both the entire range of depressive symptoms (including normal sadness in response to
loss), as well as major depression (meeting a more severe DSM-IV criteria for
psychiatric disorder over a longer period of time) (Barsevick et al., 2002). Most outcome
studies use empirically valid and structured interviewing strategies and depend on
depression severity levels to assess for major depression, which becomes especially
important when assessing for antidepressant medication and psychosocial intervention
combination (Gilbar & Neuman, 2002). Yet other studies have recommended the
inclusion of both major depressive disorder and depressive symptom categories
because the former is often difficult to establish in a cancer patient population (Razavi et
al., 1996). Other psychosocial interventions studies were unlikely to systematically rate
depression levels as normal, sub-major depressive disorder, or major depressive
disorder, and thus no information was provided about level of depression (Barsevick et
al., 2002). Furthermore, some studies do not even require the presence of depression as
an inclusion criterion for participation (Barsevick et al., 2002), and are usually considered
preventive depression studies (Williams & Dale, 2006). Previous systematic reviews and
meta-analyses have failed to distinguish between caseness for depression and
depressive symptoms (Williams & Dale, 2006).
Prevalence
Studies have identified a sizeable percentage (58%) of cancer patients have
depressive symptoms and anywhere from 38% (Cella et al., 1993) to 50% have major
depressive disorder (McDaniel et al., 1995; Strong et al., 2004), which varies by cancer
site and assessment method (Bailey, 2005). This prevalence of depression is often
difficult to obtain because studies usually differ by cancer site, disease stage, and
11
diagnostic criteria for depression (major depressive disorder versus depressive
symptoms). Depression prevalence rates and a cancer diagnosis generally include all
depressive disorders, not just major depressive disorder (Pasquini & Biondi, 2007).
Adjustment disorder is diagnosed more frequently and major depression is diagnosed
less frequently in cancer patients (Barsevick et al., 2002). It has also been shown that
the reported variability in prevalence of depression among cancer patients is attributable
to several factors: medical and personal factors related to different study methods,
instruments and procedures (Pasquini & Biondi, 2007). Nevertheless, the prevalence is
important to establish because it helps us understand the clinical significance of the
problem. Such significant prevalence and impairment caused along the cancer
continuum make these conditions a priority for research (Pasquini & Biondi, 2007).
Despite the high prevalence of depression in the United States, major depression
and depressive symptoms, are frequently underdiagnosed and undertreated (McDaniel
et al., 1995; Pandey et al., 2006; Rodin & Voshart, 1986; Somerset, Stout, Miller, &
Musselman, 2004; Williams & Dale, 2006). Although depression is commonly
encountered in medical populations, these symptoms are often clouded by
neurovegetative symptoms that may be secondary to either cancer or depression
(McDaniel et al., 1995; Reich, Lesur, & Perdrizet-Chevallier, 2008). Major depression
and depressive symptoms can further cause amplification of physical symptoms,
increased functional impairment and poor treatment adherence (Fann et al., 2008).
Prevalence among Minorities and Low-Income Groups
Although there is evidence that major depressive disorder and depressive
symptoms are common in cancer patients, we have less knowledge about the
prevalence of depression in low-income and ethnic minority cancer patients. Since
virtually all research on depression and cancer have been with non-Latino Whites, it is
12
difficult to establish prevalence of depression among ethnic minority patients
(Meyerowitz et al., 2000). In depression and cancer trials, ethnic minorities are
underrepresented (Antoni et al., 2001). Additionally, there is a paucity of literature on
direct treatment outcomes for impoverished minority populations (Perez Foster, 2007).
This minority underrepresentation makes it difficult to generalize findings to minority
populations (Weber et al., 2004), which may vary by ethnicity and socioeconomic status
(Schneider & Chiriboga, 2005).. For example, depression may be especially important to
measure in Latino (which usually refers to people of Mexican, Central American, and
South American ancestry) patients, in light of the relatively high rates of depression
reported by Latinos in the United States (Meyerowitz et al., 2000). Currently the largest
minority group in the United States, Latinos face disparities in the recognition and
treatment of major depression (Lewis-Fernández, Das, Alfonso, M.M., & Olfson, 2005).
Under-recognition of depression in adult Latino Americans may be related to language
differences, health literacy barriers, somatic presentations, and the use of cultural idioms
of distress (Lewis-Fernández et al., 2005). The need for information about the reactions
of minority cancer patients may be particularly pressing for those cancers with high
incidence rates among minority populations and for those situations in which
psychosocial difficulties are likely (Meyerowitz et al., 2000).
Despite the increased risk for low-income, ethnic minorities developing clinical
depression (McCracken et al., 1997) these groups are less likely than Whites to access
mental health services and to receive guideline-congruent depression care (Young,
Klapp, Sherbourne, & Wells, 2001). As a result of these inequities in mental health care,
The President’s New Freedom Commission on Mental Health (2003) concluded that
ethnic minorities experience a disproportionate burden of disability associated with
mental disorders and are at higher risk than whites for having their mental health needs
13
unmet. One explanation suggested for this disparity is that ethnic minority groups
conceptualize depressive symptoms as social problems or emotional reactions to
situations, while white middle class people are more apt to view depression as a disease
requiring professional treatment (Karasz, 2005). In addition, social class may greatly
affect problem-solving, given the different levels of financial need and resources
available to people confronting problems (Heppner, Witty, & Dixon, 2004).
Quality of Life among Depressed Cancer Patients
Regardless of anyone’s cultural background and despite advances in early
detection and effective treatment, cancer still remains of the most feared diseases
(Bailey et al., 2005). This is attributed to its association not only with death but also with
diminished quality of life (Bailey et al., 2005). Not only are major depression and
depressive symptoms common to cancer patients, but the disease has also been shown
to negatively affect their quality of life (Hopko et al., 2005; Marchioro et al., 1996;
McLachlan et al., 2001; Pasquini & Biondi, 2007; Petersen & Quinlivan, 2002; Reich et
al., 2008; Somerset et al., 2004; Williams & Dale, 2006). The quality of life assessment
tool is one of the most important and common parameters to measure in cancer patients
(Marchioro et al., 1996). Improvement in quality of life is a major goal within the care for
cancer patients (Wedding et al., 2008). Major depression is the most common
psychiatric disorder among cancer patients and has been shown to be associated with
diminished quality of life (Pasquini & Biondi, 2007; Reich et al., 2008; Somerset et al.,
2004; Williams & Dale, 2006) relative to non-depressed cancer patients (Deshields,
Tibbs, Fan, & Taylor, 2006; Hopko et al., 2008).
Reducing psychological symptoms is a desirable outcome as it improves the
quality of life of cancer patients (Petersen & Quinlivan, 2002). The importance of
detecting and treating depressive illness in cancer patients lies not only in the relief of
14
psychological distress, but also on its impact on quality of life (Williams & Dale, 2006).
For example, participation in a culturally and linguistically adapted cognitive-behavioral
stress management intervention improved quality of life in Latino monolingual men
treated for localized prostate cancer (Penedo et al., 2007). Nevertheless, there is the
need for randomized controlled trials that establish whether the intervention is an
efficacious, cost-effective, and easily administered in primary care treatment that
improves the overall quality of life for depressed cancer patients (Hopko et al., 2005).
Immune Response among Depressed Cancer Patients
Depression among cancer patients has also been shown to have an impact on
the biological mechanisms which decrease immune response (Figueira & Ouakinin,
2008; Holland et al., 1998; Pasquini & Biondi, 2007; Petersen & Quinlivan, 2002; Reich
et al., 2008; Somerset et al., 2004; Spiegel, 1997; Williams & Dale, 2006). Studies have
shown that depression is associated with increased impairment of immune response in
cancer patients (Holland et al., 1998; Massie, 2004; McDaniel et al., 1995; Pasquini &
Biondi, 2007; Reich et al., 2008; Somerset et al., 2004; Williams & Dale, 2006). Chronic
stress is one of the key detrimental effects on immune function that may retard the ability
of an individual with cancer to resist disease progression (Petersen & Quinlivan, 2002).
In addition to the benefits to overall quality of life, there has been increasing evidence
that reduction in depressive symptoms might also be necessary to optimize the body’s
immunological response to cancer (Petersen & Quinlivan, 2002). Among other things,
treatment of depression in these patients may improve immune function and survival
time (McDaniel et al., 1995). Recent research reviewed by McDaniel et al. has
illuminated the psychosocial and immune interactions and outcomes in patients with
cancer, and even further progression on cancer incidence and progression. Alterations
in immune function in patients with cancer are of particular interest because of the
15
potential immune-protective effect of psychobiological interventions in patients with
cancer (McDaniel et al.). The hope for treatment is that environmental factors will be
integrated with psychological and biological systems, mainly of endocrine or
neuroimmunological nature, in understanding and managing depression with medically ill
patients (Figueira & Ouakinin, 2008).
Morbidity and Depressed Cancer Patients
In general, depression has also been shown to adversely affect the course of
chronic physical illnesses including cancer (Pasquini & Biondi, 2007; Reich et al., 2008;
Sobel & Markov, 2005). In fact, untreated depressive disorders might even be linked to a
faster progression of cancer (Pasquini & Biondi, 2007; Sobel & Markov, 2005). Some
studies have reported an association between stress, immunity and cancer occurrence
(Reich et al., 2008). The presence of a co-morbid physical condition could confound this
response and further impair the ability to cope.
Adherence to Medical Treatment and Depressed Cancer Patients
Depression among cancer patients has also been shown to have a significant
impact on adherence or compliance with medical treatment (Pasquini & Biondi, 2007;
Somerset et al., 2004; Spiegel, 1997). Clinical depression and depressive disorders
have not only been found to impact the course of the disease, but also compliance with
medical therapies (Pasquini & Biondi, 2007; Somerset et al., 2004). Specifically,
depression has been shown to lead to poorer adherence to cancer treatment (Pasquini
& Biondi, 2007). This also becomes especially important to retaining patients in cancer
care as well as depression care. In addition, the nature of the relationship with
physicians also affects adjustment to the illness, satisfaction with treatment outcome,
and adherence to medical treatment protocols, which can influence relapse and survival
(Spiegel, 1997).
16
Mortality and Depressed Cancer Patients
Survival has been shown to be adversely affected by depression among cancer
patients (Onitilo, Nietert, & Egede, 2006; Pasquini & Biondi, 2007; Pirl et al., 2008; Reich
et al., 2008; Sobel & Markov, 2005; Somerset et al., 2004). The life-threatening nature of
cancer becomes a greater risk to survival when depression is involved. The coexistence
of cancer and depression is associated with a significantly increased mortality (Onitilo et
al., 2006; Reich et al., 2008; Sobel & Markov, 2005; Somerset et al., 2004). Thus, the
treatment of depression in cancer patients improves their dysphoria and other signs and
symptoms of depression, and may improve survival time (McDaniel et al., 1995).
Depression Treatment
Important progress has been made toward exploring the efficacy of psychosocial
interventions (both antidepressant medication and psychotherapy) with cancer
patients(Hopko et al., 2008). A small number of small-scale trials and studies indicated
the feasibility and effectiveness of psychotherapeutic interventions (especially cognitive
behavioral therapy (Hopko et al., 2008) in treating depressive symptoms in cancer
patients (Hopko et al., 2008; Mynors-Wallis, Gath, Lloyd-Thomas, & Tomlinson, 1995;
Razavi et al., 1996; Reich et al., 2008), at least in the short-term(Williams & Dale, 2006).
Cognitive behavioral therapy is spent enhancing an understanding of depression as it
relates to cancer. It also helps the patient to acknowledge and recognize depressive
symptoms a cancer expects to experience (Bailey, 2005). While it is not the panacea for
depression, it does assist in establishing coping and management strategies before,
during, and following treatment for cancer (Bailey). For example, Problem Solving
Treatment (PST) is a cognitively behaviorally based form of short-term psychotherapy,
which has shown to be an effective, feasible and acceptable form of psychological
treatment for major depression in primary care (Mynors-Wallis et al., 1995). PST has
17
even been shown to be as effective as some antidepressant medication (Mynors-Wallis
et al.). With regard to antidepressant medication, there is strong evidence indicates that
antidepressant medication is tolerable and effective in treating depression among cancer
patients(Simon, 2002). In fact, psychological treatment combined with antidepressant
therapy is associated with an even higher improvement rate than drug treatment
alone(Pampallona, Bollnin, Tibaldi, Kupelnick, & Munizza, 2004). Sometimes, patient
compliance with antidepressant treatment in primary care is often poor, and necessitates
the need for psychotherapeutic treatment (Mynors-Wallis et al., 1995). Without
compromising study quality, studies longer than 12 weeks showed a significant
advantage of combined treatment over drug treatment alone, with a significant reduction
in dropouts compared with non-responders. Psychotherapeutic treatment combined with
antidepressant therapy is associated with a higher improvement rate than drug treatment
alone (Pampallona et al., 2004). In longer therapies, the addition of psychotherapy helps
to keep patients in treatment (Mynors-Wallis et al., 1995; Pampallona et al., 2004).
Treatment context and providers are important components to depression
treatment for cancer patients (Christensen & Johnson, 2002; Moynihan, 1998). Mental
health services that are integrated into primary care help physicians improve patient
adherence to antidepressant medications as well as facilitate access to psychotherapies
like problem-solving that have proven feasibility and efficacy (Katon et al., 1996; Mynors-
Wallis et al., 1995). In addition, collaborative depression treatment has not only been
shown to improve adherence to cancer treatment (Dwight-Johnson et al., 2005), but also
were more effective than usual care in reducing depression and improving functioning
and accessibility to guideline-congruent care (Cabassa & Hansen, 2007). Multifaceted,
problem-solving model interventions with a multidisciplinary psycho-oncology team have
been tested and found to be effective (Strong et al., 2004).
18
Although progress has been made in advancing the knowledge base with regard
to depression treatment among cancer patients, there still exist confounds to diagnosing
depressive disorders (Fisch et al., 2003; Stokes, 1993) and determining conclusively that
depression treatment is effective for all cancer patients (Williams & Dale, 2006). Part of
the reason for this inconclusiveness is that most studies are preventive in nature, offered
to all patients. Different psychotherapeutic interventions are said to differ according to
the type of disease, and interventions must be systematically evaluated in each specific
instance (Moynihan et al., 1998). As a result, cancer patients with co-morbid major
depressive disorder frequently do not end up receiving appropriate treatment (Strong et
al., 2004) based on their depression severity. It has been suggested that patients be
selected on the basis of the diagnosis of major depression and studies should monitor
the use of multiple therapies simultaneously (e.g. both psychotherapy counseling and
medication) (Williams & Dale, 2006). It has also been suggested that a simple and rapid
means of identifying patients with moderate or severe levels of depression could be
valuable in reducing the future incidence of depression (McLachlan et al., 2001). For
example, to help reduce the number of depressed Latino patients who pass through
primary care undetected or inadequately treated, family physicians should make a
concerted effort to increase their awareness of the presentation of major depression in
this population and actively promote adherence to effective treatments (Lewis-
Fernández et al., 2005).
Depression Treatment among Ethnic Minority Populations
Primary care is an important source of mental health care for Latinos, who are
often underserved (Cabassa, 2006). Since depressed outpatients with public insurance
tend to be overrepresented in primary care clinics, clinicians in these settings need to be
particularly vigilant in recognizing depression and offering appropriate treatments
19
(Lesser et al., 2007). This perspective is echoed by the NCCN guidelines (1997), which
indicate the importance of requiring that moderate or high levels of distress be referred
to mental health, social work, or pastoral care professionals, and mild levels of distress
be referred to a primary oncology team, who will manage the problem with appropriate
resources (Barsevick et al., 2002).
Although data on intervention effectiveness and treatment adherence among
low-income, ethnic minority populations is relatively sparse due to limited inclusion of
these populations in depression studies (Miranda, Azocar, Organista, Munoz, &
Lieberman, 1996), several recent randomized clinical trials testing the effectiveness of
cognitive-behavioral and problem-solving psychotherapy models indicate that these
interventions are also acceptable and effective among ethnic minority populations
(Arean, Ayalon, Hunkeler, & et al, 2005; Arean & Gallagher-Thompson, 1996; Coulehan,
Schulberg, Block, Madonia, & Rodriguez, 1997; Miranda, Azocar, Organista, Dwyer, &
Arean, 2003; Miranda & Muñoz, 1994). Specifically, cognitive behaviorally-based
interventions with a direct problem focused, psycho-educational component have been
shown effective with African Americans (Alvidrez, Azocar, & Miranda, 1996), Latinos
(Karasz, 2005; Sahler et al., 2005), Korean Americans (Kim, Bean, & Harper, 2004),
Chinese (Chen & Davenport, 2005), Filipinos (Alvidrez et al., 1996), and Japanese and
Taiwanese (Heppner et al., 2002). In fact, cognitive-behavioral treatments including
Problem Solving Therapy (PST) have been found to be effective in treating depression
among Latinos, particularly when socio-environmental stress is a significant factor
(Comas- Díaz, 1981; Gil et al., 1996; Muñoz & Ying, 1993; Tableman, 1987).
Additionally, addressing depression using a systematic, structured, stepped approach
(which is endemic to PST) in the treatment of depression might be most beneficial with
low-income ethnic minority patients (Araya et al., 2003).
20
Given the fact that new psychotherapeutic interventions generally are developed
for and evaluated with middle-class White populations first (Alvidrez et al., 1996) more
culturally sensitive adaptations of existing treatment models for low-income ethnic
minority groups are needed (Castro-Blanco, 2005). This should be considered because
the effectiveness of a given solution can vary from person to person and across differing
settings due to differences in people’s personal goals and cultural values (Nezu, C.M.,
S.H., & et al., 1998). Tailoring interventions to individual patient needs can be
accomplished by skillfully selecting and blending strategies from several categories
according to specific clinical situations (Maliski et al., 2004). For example, to keep
Latinos in research protocols, providing culturally sensitive treatments that incorporate
families as part of recruitment efforts, (particularly older men in the family) are necessary
(Miranda et al., 1996). Integrating cultural sensitivity with more conventional treatment
approaches offers the potential for an effective, more generalizable model of intervention
(Castro-Blanco, 2005).
Depression Treatment Non-Adherence
Adherence and non-adherence become critical elements of determining the
feasibility of clinical studies, which is often evaluated in terms of rates of participation
and of adherence to the intervention program (Akechi et al., 2006). Based on a
systematic review of depression treatment studies with cancer patients, what we do
know is that dropout rates for intervention and controlled psychotherapy trials can range
up to 41% (Ayres et al., 1994). And even when an accurate diagnosis of depression
occurs and treatment with antidepressant medication is initiated, approximately 33% of
(non-cancer) patients still discontinue treatment with medications during the first month
of treatment (Katon et al., 1996; Lin et al., 1995; Simon, 1993). Additionally, the number
21
of non-cancer patients who fail to return after the first depression counseling and therapy
session can be as high as 49% (Garfield, 1994).
Adherence to depression treatment is a major obstacle in the retention of
patients with cancer in clinical trials (Rawl, 2002) and depression treatment (Lin et al.,
1995). Although a great deal of research has been conducted on the determinants of or
factors related to adherence in general (Meichenbaum & Turk, 1987), few studies have
devoted a discussion of the phenomenon of dropout in relation to cancer patients (Gilbar
& Neuman, 2002). Research on dropping out of psychological intervention programs for
other (non-cancer) types of populations (e.g. substance abuse, chronic mental illness,
and medical illness, like HIV (Davis et al., 2004; Reece, 2003), diabetes mellitus
(Harman, 2005) is more widespread (Gilbar & Neuman, 2002), with these non-cancer
related depression adherence studies including mostly adults (Khouzam et al., 1998;
McDonough et al., 1996), particularly older adults (McDaniel et al., 1995).
.
Most attention
has been directed to treatment success rather than to dropout, refusal, or relapse after
treatment (Lincoln et al., 2005). Although research is starting to show increasingly more
precision in measuring the prevalence of depression among patients with cancer and
identify efficacious treatments, less is known specifically about adherence (Breitbart,
1995; National Institute of Health State of the Science Panel, 2004; National Institute of
Mental Health, 2002; Williams & Dale, 2006)
and the barriers involved in non-adherence
among low-income minority cancer patients who are depressed.
Required registering of the details of clinical trials adherence to CONSORT
guidelines reduces, but does not eliminate bias in the literature (Coyne, Lepore, &
Palmer, 2006) . There is the potential for bias if proportionately more patients drop out
from a particular group (McLachlan et al., 2001). Studies in which adherence was not
designated as a primary outcome cannot be given the same weight as studies designed
22
with adherence as an end point. Such studies can significantly limit researchers’ ability
to determine if demographic or medical characteristics biased the sample among
completers and dropouts, which raises additional concern about the representativeness
of the sample and the generalizability of findings (Weber et al., 2004). Many studies are
analyzed on an intention to treat basis – that is, comparisons included all patients who
were randomized, according to allocated treatment, regardless of compliance
(Moynihan, 1998), which can also be problematic.
Additionally, there often exist inconsistencies with the operational definitions of
adherence and dropout (Davis et al., 2004; Nakao, 2001; National Institute of Health
State of the Science Panel, 2004; Reis & Brown, 1999; Simons, 1984), which is usually
defined by provider judgment (Acosta, 1980; Glantz, Rimer, & Lewis, 2002), and might
be different than that of the patient. Although some studies provide patient responses to
the reasons for dropout, the explanations are brief and vaguely described. In-depth
patient perspectives are critical because such patients may fail to engage in and accept
treatment for a variety of reasons rooted in their own cultural background and personal
experience (Montano & Kasprzyk, 2002), which are important to understand in order to
develop strategies for addressing the problem.
Many studies have used the term “dropping out” (Antoni et al., 2001; Classen,
2001; Dwight-Johnson et al., 2005; Edelman, 1999; Fisch et al., 2003; Fukui, 2000;
Given et al., 2004; Goodwin et al., 2001; Kuijer, Buunk, DeJong, Ybema, & Sanderman,
2004 ; Musselman et al., 2001; Ogrodniczuk et al., 2005; Rawl et al., 2002; Razavi et al.,
1996; Sandgren, 2000; Van Heeringen & Zivkov, 1996; Winzelberg, 2003); which tends
to be the most common reference to the general decision to end therapy, contrary to
both the therapist’s current recommendation and the initial agreement between patient
and therapist (Ogrodniczuk et al., 2005). The terms “compliance” (Fisch et al., 2003;
23
Marchioro et al., 1996; Razavi et al., 1996) and “adherence” are often used
interchangeably when studying behavior change in the context of controlled clinical trials
that examine planned interventions, in which a motivational aspect to the definition of
terms underscores a difference beyond mere semantics (Brawley & Culos-Reed, 2000;
Gehlert & Browne, 2006). Adherence is defined broadly as the extent to which a
patient’s behavior corresponds with medical (Meichenbaum & Turk, 1987), and implies
that people freely choose to undertake behavioral plans, have input to them, and have
collaborative involvement in developing and adjusting their plans, which implies a more
active role in motivation, than does “compliance” (Brawley & Culos-Reed, 2000). Other
terms used to describe cancer patients who dropout of depression treatment or studies
are: “non-adherence” (Meichenbaum & Turk, 1987; Shea, 2006), “premature
termination” (Van Heeringen & Zivkov, 1996), “discontinuing” (Razavi et al., 1996),
“declined participation” (Burton, 1995; Ell et al., 2008; Moynihan, 1998; Petersen &
Quinlivan, 2002), “refusing” (Burton, 1995; McArdle et al., 1996; Moynihan, 1998) or
“withdrawing” (Given et al., 2004; Morrow et al., 2003; Rawl et al., 2002; Razavi et al.,
1996; Roscoe, 2005; Van Heeringen & Zivkov, 1996; Weber et al., 2004).
One of the primary problems with the current state of adherence and dropout
literature, is that differences in methodology (Johansson & Eklund, 2006; National
Institute of Health State of the Science Panel, 2004; Reis & Brown, 1999) often creates
highly contradictory results that are difficult to reconcile (Coday et al., 2005; Reis &
Brown, 1999). Different designs and analysis strategies adopted to handle patient
attrition and the amount of information reported on these patients vary considerably
between studies (Simon, Levine, Lustman, & Murphy, 1984). Such studies use
predominately quantitative study methods and include: retrospective analysis (Harman,
2005; Reis & Brown, 1999), survey instruments (Hilderbrandt, Steyeberg, Stage,
24
Passchier, & Kragh-Soerensen, 2003; Lenze et al., 2001; Tutty, Ludman, & Simon,
2005), systematic reviews (Pampallona et al., 2004; Simon et al., 1984)
, observational
studies (Sher, McGinn, Sirey, & Meyers, 2005), cross-sectional, meta-analysis
(Pampallona et al., 2004) and randomized clinical trials (Fukui, 2000; Van Heeringen &
Zivkov, 1996). Most depression treatment efficacy studies that involve cancer patients
are review articles (Lepore & Coyne, 2006) or non-randomized studies (without a control
arm). Without a control condition, we cannot precisely discern the actual effectiveness of
the program (Akechi et al., 2006). Most of the review studies depend on non-randomized
trials of psychotherapeutic or counseling interventions for depression in cancer patients
to determine the efficacy or feasibility of interventions (Akechi et al., 2006; Lepore &
Coyne, 2006). In addition, when using randomized trials from high-impact journals, there
is evidence of confirmatory bias, selective reporting of the most favorable of multiple
outcome measures, suppressing of null results in subsequent citations of trials, and
dropping of data for patients least likely to benefit from the intervention (Coyne et al.,
2006).
Depression Treatment Non-Adherence and Ethnic Minority Populations
Cancer patients are a segment of the population of low-income minorities with
depression that has gained less attention in research on depression treatment
adherence. Existing literature is of little use in helping us understand why low-income,
minority cancer patients adhere or do not adhere to depression treatment. Depression
treatment is a particularly important element in cancer care, especially in communities
where there are cultural and psychosocial issues related to detection, diagnosis and
treatment. Although there is some research concerning minority depression treatment
adherence rates, the applicability to cancer patients is questionable. Existing studies of
antidepressant medication and counseling depression raise questions as to the
25
automatic applicability of findings to minority populations from research that was
conducted among predominantly White subjects (Olfson et al., 2006; Yancey, Ortega, &
Kumanyika, 2006), failed to describe the ethnic composition of the sample, completely
omitted discussion of ethnic minority sample description, were not conducted
(Thormahlen, 2003) in public care systems, or provided little explanation of reasons for
dropout (Davis et al., 2004; Ogrodniczuk et al., 2005). Studies which included
exclusively non-minority populations acknowledge that success of the intervention when
there is such a high adherence, might not generalize to other socioeconomic or ethnic
minority groups (Olfson et al., 2006).
Although, it is unavoidable in depression treatment and trials that some patients
will be lost, measures should be taken to minimize dropout rates and to account for
missing data for the findings to be valid and generalizable (Mathibe, 2007). Cancer
patients often withdraw from treatment for many reasons, and this leads to various
consequences such as weakening of the statistical power to detect the differences and
the effects of treatment protocols (Mathibe, 2007; McLachlan et al., 2001). Many
adherence studies were analyzed on an intention to treat basis – that is, comparisons
included all patients who were randomized, according to allocated treatment, regardless
of compliance (Moynihan, 1998), which can be problematic.
Unfortunately, there are few studies that examine minority patients with cancer,
covariates of compliance, or that test interventions to improve compliance (Dwight-
Johnson et al., 2005), among depressed cancer patients. However, when looking at
ethnicity, few studies have found consistent predictors of non-adherence and dropout
(DiMatteo, 2000). In mental health specialty care, ethnic minorities, particularly Latinos,
have high rates of dropout, missed appointments and poor medication adherence (Shea,
2006; Vega et al., 2007). Low rates of study participation by these groups become
26
obstacles to research on the health of diverse populations in the United States
(Sweeney et al., 2007). For example, 42% of white patients, but 86% of nonwhite
patients, used a sub-therapeutic dose of antidepressants (Wells, Katon, Rogers, &
Camp, 1994). Non-white depressed patients, particularly African Americans, used
antidepressant medications at one-third the rate of white patients, when controlling for
socioeconomic status (Wells et al., 1994). Research indicates that antidepressant
medication discontinuation (during the first 30 days of treatment) is significantly more
common among Latinos (53.8%) than among non-Latinos (41.3%) (Olfson et al., 2006)
and Latinos are less likely than are Whites to find antidepressant medication acceptable,
but are more likely to find counseling acceptable (Burton, 1995; Fukui, 2000), although
there is still greater psychotherapy dropout, in comparison to Whites (Hilderbrandt et al.,
2003; Massie, 2004). Despite this evidence, we still lack adequate empirical research
and information on minority adherence and treatment outcome studies within the context
of a cancer diagnosis (Holland et al., 1998).
Although we have some information about the characteristics involved with
depression treatment dropout among non-minority, non-public sector, educated cancer
patients, our knowledge base is limited with regard to the interaction between a different
context and population. We do know that perceived stigma, family perceptions, and
practical barriers such as cost and transportation to therapy may also impede receipt of
care among low-income and minority populations (McClure, Catz, & Brantley, 1999).
This becomes especially important when considering culturally-based preferences for
depression care and customizing interventions to retain low-income and minority hard-
to-reach populations to depression treatment.
27
Factors Affecting Retention to Depression Treatment
Given the consequences, considerable attention should be devoted to identifying
characteristics associated with non-adherence, which will assist with the design and
implementation of intervention strategies that might facilitate adherence (Christensen &
Johnson, 2002). Specifically, reasons for non-adherence of antidepressant medication
treatment are often complex and have more than likely changed over time as social and
cultural changes have occurred and medical practice has evolved. To date, there has
been little concrete identification of specific factors leading to dropout (Ogrodniczuk et
al., 2005), particularly from depression treatment among cancer patients. According to
mainly descriptive (Ogrodniczuk et al., 2005; Yancey et al., 2006), quantitative studies,
there are some variables which seem to be related to depression treatment adherence.
However, most existing correlational adherence literature is somewhat tentative in
nature (Meichenbaum & Turk, 1987). Based on Meichenbaum & Turk’s adherence
dimensions, significant non-adherence correlates to depression treatment among cancer
patients include the following: 1) characteristics of the patient, 2) characteristics of the
treatment regimen, 3) features of the disease, 4) the relationship between the health
care provider and the patient, and 5) the clinical setting. These dimensions are also
consistent with a well-known model, Andersen and Newman’s Individual Determinants of
Health Service Utilization (2005) which posits that health services use is determined by
societal factors, health services system factors, and individual factors, which has a
similar ecological perspective as Meichenbaum & Turk (1987) categorization.
One of the most important and credible levels of evidence consists of
randomized controlled trials (Andrykowski & Manne, 2006). Williams and Dale’s (2006)
systematic review of 24 randomized controlled trial studies, is noteworthy on the topic of
depression treatment (either antidepressant medication or psychotherapy) in adults with
28
cancer, most importantly because of the distinguishing categories for caseness of
depression/depressive symptoms. A further review of each of these studies was
conducted to look specifically at drop-out rates, specifics about the population
description, if the study was statistically tested for the demographic and clinical
correlates of dropout, and the reasons for dropout. Various terms for describing attrition
in these 24 studies were: “withdrew”, “compliant”, “discontinue”, “dropped out”, “non-
compliant”, “declined.” Among the 24 reviewed studies, 18 were solely
psychotherapeutic or counseling depression treatment, with 17/18 being preventive
studies. The psychotherapy treatment tested included group therapy, cognitive
behaviorally based treatment, education, internet support, telephone support, supportive
expressive group therapy, relaxation counseling, and problem-solving treatment. Six of
the 24 studies were solely antidepressant medication depression treatment. Participant
study populations ranged from 30 to 549 cancer patients, with a variety of different types
of cancers. Few studies were conducted in public system care sectors. There were few
reported minorities in any of the studies, with most of the studies including White, middle
income, educated, working patients. Although most of the studies did not test for
demographic and clinical characteristics statistically associated with adherence (13/24),
all studies provided brief reasons for attrition from patients. If we use Meichenbaum &
Turk’s (1987) classic adherence framework, reasons for attrition from Williams and Dale
reviewed studies and a few similar studies, we can categorize the correlates into 3
dimensions of barriers to treatment. The first dimension is characteristics of the patient,
which include gender(Simon, 1993), age (Miller, Chibhall, Videen, & Duckro, 2005;
Sweeney et al., 2007; Tutty et al., 2005), practical issues (Dwight-Johnson et al., 2005;
Meichenbaum & Turk, 1987; Meyerowitz et al., 2000; Razavi et al., 1996; Van Duyn et
al., 2007), poverty (Meyerowitz et al., 2000; Van Duyn et al., 2007), immigration
29
(Meyerowitz et al., 2000), transportation (Dwight-Johnson et al., 2005; Van Duyn et al.,
2007) , neighborhood safety (Van Duyn et al., 2007), childcare (Dwight-Johnson et al.,
2005; Van Duyn et al., 2007), and social support (Gilbar & Neuman, 2002; Meyerowitz et
al., 2000; Strong et al., 2004) . The second dimension is characteristics of the
depression treatment and cancer treatment regimens, which include the side effects
from both chemotherapy (Bailey, 2005; Holland et al., 1998; Johansson & Eklund, 2006;
Pandey et al., 2006) and antidepressant medication treatment (Lin et al., 1995; (Razavi
et al., 1996) et al., 1996; Stokes, P., 1993; Thompson, J. Ranking, H, Aschcroft, CW, et
al.). The third dimension is features of the disease, which include the medical/physical
cancer symptoms (Barsevick et al., 2002; Lin et al., 1995; Sobel & Markov, 2005),
fatigue (Morrow et al., 2003), cognitive impairment (Ayalon, Arean, & Alvidrez, 2005;
Dwight-Johnson et al., 2005); , and delirium (Okamura et al., 2008). There were neither
any identified correlates between depression treatment non-adherence among cancer
patients and the health care provider and the patient, nor the clinical setting.
Correlated Characteristics of the Patient
With regard to cancer correlates and depression treatment dropout, gender was
found to be a weak predictor of medication treatment adherence (Simon, 1993),
however, depression was positively associated with cancer in men who felt hopeless
(Schneider & Chiriboga, 2005). There are some discrepancies with regard to age
correlates and depression treatment. Some literature reports that, compared with
compliant patients with a life-threatening medical condition, non-compliant patients were
much younger (Miller et al., 2005) (e.g. ages 18-22), which may be due in part to
difficulty in reaching them on the telephone (Tutty et al., 2005). However, other research
reports that older age negatively affects participation in studies; the extent to which this
30
holds true in U.S. Hispanics, who have a younger average age than the non-Hispanic
population, has not been examined in detail (Sweeney et al., 2007).
Health and mental health is only one, albeit an important, component of life
(Meichenbaum & Turk, 1987). However, patients enter into the health care relationship
with certain commitments, demands, and life circumstances that may be more salient
than their health problems and may potentially interfere with the performance of specific
health and mental health behaviors (Meichenbaum & Turk, 1987). Although many
practical issues have not been found to be statistically correlated with adherence,
studies have reported that these are concerns, primarily with minority and low-income
patients. The objectives of depression treatment and/or control may be quite different for
cancer patients who experience numerous daily stressors related to oncological
treatments (Razavi et al., 1996). A possible explanation has been suggested that
perhaps ongoing stressors in patients’ lives, such as those related to poverty and
immigration, are so overwhelming that cancer cannot become the primary focus
(Meyerowitz et al., 2000). Even with other types of behavioral studies (e.g. increasing
physical activities), barriers are often common to all underserved people (i.e. lack of
time, transportation, neighborhood safety, or economic resources) (Van Duyn et al.,
2007). Two of the mostly commonly reported practical patient-level barriers to
depression care were child care or other family responsibilities and transportation
problems (Dwight-Johnson et al., 2005; Van Duyn et al., 2007), related to time and
distance concerns (Sharpe et al., 2004). It is important to remember that cancer occurs
in a broader context of the patients entire lives.
Social support is another important determinant of patient adherence to
treatment. Research on social support suggests that it is not the number of social
contacts or that the contacts are necessarily from one’s family, but rather the quality of
31
the relationship that influences the individual’s ability to cope with distress and adhere to
treatment is what is most important. As such, there is somewhat inconsistent data about
cancer patients who comply with psychological interventions having more or less social
support than those who drop out (Gilbar & Neuman, 2002). Studies acknowledge that
social support can be a barrier or enabler to depression treatment. Many authors have
identified “familism,” strong interdependence within the extended family system, as a
primary coping resource in the Latino community (Meyerowitz et al., 2000). However,
dropouts have also been shown to be four times as likely as completers to report having
important family problems at baseline (Dwight-Johnson et al., 2005). It is important to
recognize that this is expected, as cancer can place new stress on the social system and
families are likely to also be impacted by the presence of cancer in their loved one
(Bailey, 2005; Meyerowitz et al., 2000). Thus, patients may require additional support
(Meyerowitz et al., 2000). Often, to replace this personal family support void, there is a
surrogate source of support that will be sought out in times of need. For example, a
multifaceted, problem-solving model intervention with a peer support component, was
tested and shown effective (Strong et al., 2004). However, not all patients responded the
same way with such support. For example, non-compliant patients with life-threatening
medical conditions, like cancer, who attended support groups tended to be somewhat
less involved in group activities than the compliant patients (Miller et al., 2005). Thus, it
is important to identify the patients’ nature of support rather than to its mere presence
(Meichenbaum & Turk, 1987), based on the context of the lives and personal
preferences, which can be an important factor in mitigating depression in cancer patients
(McDaniel et al., 1995).
32
Correlated Characteristics of the Treatment Regimen
Generally,the more complex the demands of treatment (e.g. chemotherapy
treatment and antidepressant medication regimens), the poorer the treatment adherence
rates (Meichenbaum & Turk, 1987). For example, chemotherapy for cancer is an intense
and cyclic treatment associated with a number of side-effects (Pandey et al., 2006).
Gynecological cancer patients experience the impact of multimodality of treatment (e.g.
surgery, radiation therapy, and chemotherapy) and the length of therapy, which are often
associated with increased risk of depression (Bailey, 2005). In a study of breast cancer
patients, high scores on depression were shown to be correlated with chemotherapy
treatment non-compliance (Johansson & Eklund, 2006; Richardson & Sanchez, 1998).
With regard to antidepressant medication treatment, the lack of patient
adherence is a formidable problem, especially with long-term medication regimens
(Katon et al., 1992). Early discontinuation of anti-depressant medications have been
shown to be mainly due to severe (Lin et al., 1995) and unpleasant side effects (Razavi
et al., 1996; Thompson, Ranking, & Aschcroft, 1982). When patients who had
discontinued taking antidepressant medication were asked retrospectively why they had
quit, the most common reason for discontinuation was that they disliked the side effects
(Lin et al., 1995). In this same study, approximately 62% of early quitters and 66% of late
quitters cited problematic side effects (Lin et al., 1995). It was reported that side effects
associated with antidepressants lead to discontinuation of therapy or a lack of patient
compliance, with twice as many clinical trial patients discontinuing treatment because of
side effects (Stokes, 1993). Some patients who discontinue therapy report that they
were not interested in involvement because they believed the medication was not
working, that they were feeling better, or that they did not need the medication (Katon et
al., 1996; Lin et al., 1995; Thompson et al., 1982).
33
One suggested way of addressing this issue is to ask patients about prior
experience with antidepressants, which have been shown to be related to early
adherence (Lin et al., 1995). Patients who received the following five specific educational
messages: 1) take the medication daily; 2) antidepressants must be taken for 2 to 4
weeks for a noticeable effect; 3) continue to take medicine even if feeling better; 4) do
not stop taking antidepressant without checking with the physician; and 5) specific
instructions regarding what to do to resolve questions regarding antidepressants – were
more likely to comply during the first month of antidepressant therapy (Lin et al., 1995).
Thus, it is very important that future studies include recording and reporting of adverse
effects, especially as such effects are likely to have an impact on compliance (Stokes,
1993).
Correlated Features of the Disease
Confounding clinical barriers to treatment in cancer patients have been
addressed in studies, as it relates to the patient’s medical condition ((Barsevick et al.,
2002). Patients with cancer have many physical symptoms caused by the disease
(Barsevick et al., 2002), e.g. fatigue (Morrow et al., 2003), which can have debilitating
adverse reactions that may cause participants to withdraw from the study (Mathibe,
2007; Sobel & Markov, 2005). In addition, unintentional non-adherence was associated
with greater cognitive impairment (Ayalon et al., 2005), like forgetting appointments
(Dwight-Johnson et al., 2005), and delirium, which was the most frequent reason for
dropout in advanced cancer patients with major depressive disorder(Okamura et al.,
2008). Refused enrollment in depression treatment and trials are said to occur due to
being too medically ill (Katon et al., 1996). Even if patients believed that a psychosocial
intervention would help them, they were so preoccupied with medical treatment when
they were offered it (Gilbar & Neuman, 2002), that they chose to decline follow through
34
with treatment. In fact, adherence was less favorable among patients who reported
greater overall disability (Lin et al., 1995).
Depression Treatment Completers
As a measure of comparison, some studies which have examined depression
treatment dropout have also gone a step further to identify correlates associated with
completion of treatment. This method of comparison is not only highly regarded to
increase the internal validity of the findings (Boeije, 2002), but also becomes especially
useful when examining characteristics which kept patients in treatment and designing
interventions to help continue to retain these patients and try to avoid their dropout from
treatment. It has been found that some cancer patients’ who left the depression
treatment intervention or treatment had more psychosocial dysfunction (Moynihan, 1998)
and significantly higher depression scores at baseline than those who completed the
study (Given et al., 2004). Despite this finding, psychosocial depression interventions
with cancer patients also find no differences between the demographic and clinical
characteristics of dropouts and completers (Fukui, 2000; Moynihan, 1998). For example,
in a study of Latina breast or cervical cancer patients, there were no significant
differences found between completers and dropouts in cancer site, cancer stage,
depression diagnosis, demographic characteristics, or baseline Functional Assessment
of Cancer Therapy scores. However, dropouts were 4 times as likely as completers to
report having important personal or family problems at baseline (Dwight-Johnson et al.,
2005). Although it is useful to generalize completer correlates and factors and enablers
of depression treatment, it should also be cautioned that completers in different contexts
may differ according to the type of disease, and interventions used within each specific
context (Moynihan, 1998). Additionally, we cannot practice the treatment of all patients
with a given disorder or target problem (e.g. depression in cancer patients) as a
35
homogeneous group, ignoring potential differences among them (Christensen &
Johnson, 2002).
Provider Retention Strategies
Despite challenges in implementation related to participant retention and
intervention delivery (Zayas, Mckee, & Jankowski, 2004), the literature suggests that
dropout can be avoided with simple provider strategies (Ogrodniczuk et al., 2005).
However, there are a couple of problems with these studies: few are empirically derived
strategies on how to deal with attrition or non-adherence, and many lack the integration
(Meichenbaum & Turk, 1987) needed to improve delivery and enhance the collaboration
(Rejeski, Brawley, McAuley, & Rapp, 2000) in a multidisciplinary setting. This is
important given the multiplicity of factors, which are likely to be involved in patients’
decisions to dropout of treatment prematurely (Ogrodniczuk et al., 2005). Because of the
complexity and the multi-dimensional nature of treatment, non-adherence and the
heterogeneity of the patient population, there is an increasing recognition that integrative
strategies are required (Meichenbaum & Turk, 1987). Despite the fact that the barriers to
treatment are discussed as discrete factors, it will become obvious that there is actually
substantial overlap that should not be viewed as totally independent (Meichenbaum &
Turk, 1987). Although no single prevention strategy will be effective across all patients or
situations (Ogrodniczuk et al., 2005), literature suggests that strategies can be
categorized according to the barriers to depression treatment. There is evidence that
interventions combining cognitive, behavioral, and affective components may be more
effective at adherence efforts, than single-focus interventions (Christensen & Johnson,
2002). As such, there still need to be additional methods for actively monitoring non-
adherence (Lewis-Fernández et al., 2005) and optimizing adherence (Bowen, Cartmel,
Barnett, Goodman, & Omenn, 1999; Coday et al., 2005) and testing their efficacy.
36
Although researchers have begun to identify demographic and clinical correlates
of dropout, few studies have tested strategies to decrease depression treatment dropout
(Ogrodniczuk et al., 2005), especially among low-income ethnic minority cancer patients
in the context of a public sector care system (Zayas et al., 2004). Identification of simple,
effective (Pampallona et al., 2004), feasible, and culturally sensitive means of retaining
minorities to cancer care are needed (Davis et al., 2004; Miranda, Chung et al., 2003;
Raison & Miller, 2003). To date, there are ample studies on recruitment (Coday et al.,
2005) of ethnic minorities (Davis et al., 2004; Miranda, Chung et al., 2003; Raison &
Miller, 2003) to depression treatment, but far fewer studies which have specifically
addressed the difficulties encountered in retaining participants to such treatment, with a
focus on practical strategies (Coday et al., 2005). Especially relevant to non-English
speaking minority groups and those with lower literacy levels, there is the issue of
effectively communicating information. It is especially important to address
communication barriers among limited-English-proficient patients, which can improve
satisfaction with physician communication and care (Gany et al., 2007). Such issues
may be due to language, patient education level and/or health literacy factors. To
combat this communication problem, it has been suggested that health communication
mechanisms incorporate some level of education for the patient and family, as well as
specific educational messages (Lin et al., 1995) which correct the misconceptions that
will most likely adversely impact compliance (Delgado, 2000). In addition, it has been
shown that improving provider-patient communication about medications, educating
families about pharmacologic treatments, and increasing families’ ability to monitor and
support medication use by family members (Vega et al., 2007), is an effective means to
facilitating effective communication and understanding. The implication is that
37
prescribing is not enough; explanation and monitoring are also required (Sharpe et al.,
2004).
The trend toward placing more responsibility on the clinician to obtain compliance
or adherence to treatment has resulted in several strategies: explaining the illness and
the rationale for the use of treatment, inquiring into the patient’s hesitation and fears
concerning medication, and using various educational approaches with the patient and
the patient’s significant other concerning possible side effects (Fawcett, 1995). Authors
argue that health-care providers must encourage questions, provide information, and
build strong partnerships with participants (Rejeski et al., 2000). Studies show that there
are simple, effective, and feasible means of retaining patients to depression treatment
(Pampallona et al., 2004), for example asking about prior experience with
antidepressants and discussions about scheduling pleasant activities also were related
to early adherence (Lin et al., 1995). Such studies use communication to help educate
their patients and strengthening the establishment of a therapeutic alliance (Meissner,
1996). Strategies which have been used to improve management are: focusing on
selected critical communication behaviors, decreasing the complexity of information,
using concrete examples, limiting the number of topics covered in one session, and
avoiding jargon (Legato et al., 2006). It was shown that improvement in patients with low
literacy did not necessarily depend on spending more time with them, but the quality of
the delivery of vital information, was most important (Legato et al.).
The few existing retention strategy studies report that treatment maintenance and
relapse prevention strategies include an element of remaining in touch with patients
through routine telephone or written follow- up (Miranda, Duan et al., 2003; Ruskin et al.,
2004; Tutty et al., 2005; Young & Maher, 1999), openness,
and flexibility on the part of
the provider (Delgado, 2000) during treatment sessions (Young & Maher, 1999). In fact,
38
it has been shown that remote treatment of depression, by means of tele-psychiatry
compared with in-person treatment of depression, show comparable outcomes and
equivalent levels of patient adherence, patient satisfaction, and health care cost (Ruskin
et al., 2004). The use of telephone assessments have been suggested as an additional
method for addressing the problem of missing data in future research (Fisch et al.,
2003). In addition, the feasibility and efficacy of implementing a telephone
psychotherapy program in primary care settings may be even valuable for enhancing
standard pharmacotherapy treatment of adult depression, especially among populations
facing greater barriers of care (Tutty et al., 2005). Importantly, the identification of
strategies in retaining participants derived from outcome research trials can also be
applicable to the difficulties in retaining patients to real world depression treatment
settings.
Theoretical Foundations
A theory is needed in part to focus on key components that have been shown to
be important, to focus the methodology, to offer some explanation of what worked, and
to help guide intervention strategies. In order to understand what facilitates or detracts
from adherence in clinical practice or in controlled clinical trials, one has to examine
studies which not only include diverse populations (Brawley & Culos-Reed, 2000), but
also that include a theoretical framework to provide an understanding and predicting
future adherence and non-adherence. Meichenbaum & Turk (1987) provide one of the
first and only attempts to use an adherence framework for identifying and categorizing
correlates to medical and mental health treatment. Once these barriers or enablers to
treatment have been established, they can be placed within a theoretical framework that
can help us in understanding and predicting why patients drop-out of treatment and help
the patient remain in treatment.
39
Despite attempts at adapting existing health behavior frameworks to adherence,
we still are specifically in need of theoretically meaningful health behavior models that
are relevant to all cancer patients (Meyerowitz et al., 2000). While existing studies are
extremely important in understanding of the prevalence and scope of the factors
involved with dropout, such studies have been mostly atheoretical in nature (Reece,
2003). Absent in the literature is a true guiding theoretical framework essential to
understanding and subsequently intervening with dropout behavior (Fukui, 2000;
Garfield, 1963) . Although related studies have used mostly individual models of health
decision-making, e.g. the Health Belief Model (Givens et al., 2006; Reece, 2003; Sher et
al., 2005) in explaining dropout, these models tend to be somewhat incomplete due to
the limited focus on individual thought processes (i.e. perception) in predicting health
and mental health behavior (Glantz et al., 2002).
Although there are an array of theories that have been applied in past studies,
there seem to be 2 overarching categories or dimensions of theories which have been
used to investigate health behavior and predict adherence with other types of
populations. The most commonly investigated theories of health behavior that can help
predict adherence and incorporate elements of outcome expectancies, outcome values,
self-efficacy expectancies, and intentions include the following: Hochbaum’s (1958)
Health Belief Model, Fishbein’s (1975) Theory of Planned Behavior (TPB), Roger’s
(1975) Protection Motivation Theory, Bandura’s (1977; 1986) Social-Cognitive Theory,
and Strecher, DeVellis, Becker, and Rosenstock’s (1986) Self-Efficacy (Brawley &
Culos-Reed, 2000). In addition, theories are also used to predict adherence to treatment
and address the processes of behavior change: Bandura’s (1977; 1986) Social-
Cognitive Theory, Marlatt and Gordon’s (1985) Relapse Prevention Model, Prochaska’s
(1979) Transtheoretical Model and Weinstein’s (1988) Precaution, Adoption, Process
40
Model (Brawley & Culos-Reed, 2000; 1988). Additionally, a motivational component is
central to most theories used to study health behavior for either prediction or behavior
change purposes(Brawley & Culos-Reed, 2000). Christensen & Johnson’s (2002) offer a
simple linear model of understanding patient adherence with medical treatment
regimens, which is derived from previous theory and research in personality, social, and
clinical psychology concerning the value of an interactionalist perspective. Although
somewhat limited, the core tenets of this framework are that factors which influence
adherence, can be better understood through the interactive patients’ characteristics,
types of adherence intervention, and illness characteristics and medical treatment
context. This array of theories includes aspects of social, cognitive, personal agency and
environmental constructs shown to be predictors of adherence.
New ideas for adherence interventions should first be examined within the
context of existing theories (e.g. TPB) and established results (Brawley & Culos-Reed,
2000). Foundational adherence theories and previous literature can significantly
contribute to the knowledge base for guiding the development of new adherence
interventions (Pinto & Floyd, 2008). Specifically with regard to this study, the TPB
provides a solid knowledge base within an organized framework, from which to begin
addressing essential findings and concepts from this literature review about depression
treatment adherence among low-income and minority cancer patients. This theory
focuses on theoretical constructs that are concerned with individual motivational factors
as determinants of the likelihood of performing a specific behavior (Montano & Kasprzyk,
2002). The TPB includes measures of attitude and social normative perceptions (that
determine behavioral intention, which in turn affects behavior) and perceived control
over performance of the behavior (Montano & Kasprzyk, 2002). The TPB includes the
following eleven constructs: Behavioral beliefs, Evaluations of behavioral outcomes,
41
Normative beliefs, Motivation to comply, Control beliefs, Perceived power, Attitude
toward behavior, Subjective norm, Perceived behavioral control, Behavioral intention,
and Behavior. This theory assumes that all other factors including demographics and
environment operate through the model constructs and do not independently contribute
to explaining the likelihood of performing a behavior (Montano & Kasprzyk, 2002). This
literature review identified many key adherence components that can be applied to TPB
theoretical constructs.
The TPB is well-suited to guiding an inquiry of the factors involved with the
Behavior of depression treatment non-/adherence. The premise of the full TPB model is
based on Behavioral Intention, which is the person’s subjective probability that the
behavior in question will be performed (Montano & Kasprzyk, 2002), in this case that
behavior being: adherence to depression treatment. As such, intention is assumed to be
the most powerful predictor of change and if intention can be changed, you can change
behavior. Intention is thought to be shaped by 3 primary factors: Attitude toward the
behavior (made up of Behavioral beliefs and Evaluations of behavioral outcomes),
Subjective norm (made up of Normative beliefs and the Motivation to comply), and
Perceived behavioral control (made up of Control beliefs and Perceived power). In the
next chapter, Table 1 provides the meaning of each theoretical construct, in relation to
this corresponding literature. For example, the TPB posits that, not only is it important to
understand individual perspectives and intentions, but also to look at the influence of
multidimensional, environmental, physical and social factors (Alvidrez & Arean, 2002;
Chyun, Amend, Newlin, Langerman, & Melkus, 2003; Cooper, Hill, & Powe, 2002; Glantz
et al., 2002; Green, Richard, & Potvin, 1996; Moreno-John et al., 2004; Pierce, Chadiha,
Vargas, & Mosley, 2003). These multidimensional elements are inherent in the
Subjective Norm construct of the TPB (Fishbein, 1967; Fishbein & Ajzen, 1975) and the
42
Environment construct in Social Cognitive Theory (Bandura, 1986, 1994)
. As such, the
quality of one’s social networks (Van Heeringen & Zivkov, 1996), family time
commitments, family obligations, and family conflict can limit or enable dropout (Brown &
Topcu, 2003; Fouad et al., 2001). Not only do others in our environment contribute to our
decisions we make, but also our beliefs about the resources, opportunities, obstacles,
and impediments in our lives will influence our decision to follow through with treatment,
which is an additional important consideration in the Perceived Behavioral Control
construct (Fishbein, 1967; Fishbein & Ajzen, 1975). Despite the often blurring of
constructs, the TPB (Fishbein, 1967; Fishbein & Ajzen, 1975) seems to be the most
appropriate existing theoretical model which considers the inherent nature of individual
cost-benefit analysis, whereby the individual dropout must weigh the literal and figurative
“cost” of dropping out of depression treatment with the benefits of remaining and
completing full course of treatment (Delgado, 2000; Holland et al., 1998). This theory
can be used to inform the intervention and help in identifying the “active” ingredients of
those interventions (Pinto & Floyd, 2008). Thus, the TPB is a promising approach to a
further exploration and understanding of adherence to treatment.
Further, the experience of cancer does not escape the influence of cultural
factors (Holland et al., 1998). Because cancer is a disease that is not always well
understood by many people, it is subject to have different meanings attached to it
(Bailey, 2005). One of the most common reactions a patient may experience is
depression regardless of their cultural orientation (Bailey, 2005). Beliefs about the
causes of cancer and its symptoms may affect the types of treatment (Holland et al.,
1998)sought
28
for depression and/or cancer, as culture provides unwritten definitions of
how families respond (Bailey, 2005). Especially when studying racial and ethnic minority
groups, it is important to emphasize important elements of cultural relevance because
43
many of the barriers to retention occur within a socio-cultural context that impacts one’s
beliefs and attitudes (Wells & Zebrack, 2008). Many individuals from these groups have
views that differ from Western biomedical models (Schraufnagel, Wagner, Miranda, &
Roy-Byrne, 2006), particularly with regard to stigma (Garfield, 1963; Reece, 2003) about
antidepressant medication and counseling. For example, within Latino cultures,
depression is often presented in terms of somatic and physiologic symptomatology ,
which is known to be more socially acceptable and avoids the stigma of mental illness
(Bailey, 2005). Additionally, the nonsharing of illness representations, treatment, and
appraisal rules are common for non-Western patients who access Western as well as
traditional medicine (Leventhal, Diefenbach, & Leventhal, 1992). As such, Cultural
Explanatory Model adds explanatory theoretical power to the TPB model in the study of
depression dropout for ethnic minority populations (Kleinmann et al., 1978). This socio-
cultural framework assumes that cultural and social-contextual factors will interactively
shape the clinical process and patient outcomes. For example, fatalistic beliefs are
generally believed to be more often associated with ethnic/ racial minority patients than
Caucasian patients, and this may similarly affect health services use (Schraufnagel et
al., 2006). In general, a patient’s past experiences, both cultural and personal, will
strongly influence their beliefs, which in turn will shape their attitudes and/ or preferences
(Schraufnagel et al., 2006). Attitudes or preferences will strongly determine someone’s
acceptance of treatment as well as their motivation (inherent in the Subjective Norm
component of the TPB) to continue treatment. This will, in turn, influence the likelihood
that a person will seek or dropout of treatment (Schraufnagel et al., 2006). Bernal’s
Ecological Framework (1994) goes a step further in serving as a guide for developing
socioculturally sensitive treatments and adapting existing psychosocial treatments to
specific ethnic minority groups (Bernal & Castro, 1994).This general theoretical
44
framework consists of eight dimensions of treatment interventions: language, persons,
metaphors, content, concepts, goals, methods, and context, which can used to inform
future depression adherence adaptations. Taken together, it is important to produce
more clinical findings, which can contribute toward a clearer understanding of how
individuals from other cultures need to have their adaptive efforts to cancer understood
within the context of their own culture (Spinetta, 1984). Thus, heightened awareness of
patients’ cultural perspectives and care preferences is important in assessment,
diagnosis, treatment (Bailey, 2005; Wells & Zebrack, 2008), and a decision to stop
taking medication or participate in psychotherapy.
45
Chapter 3: Research Methodology
Research Aims
Using former participants from a randomized clinical depression treatment trial of
cancer patients - Alleviating Depression among Patients with Cancer (ADAPt-C) (Ell et
al., 2007) this study explored barriers and factors associated with depression treatment
study non-adherence and dropout among low-income minority cancer patients and
identify strategies used by study trial providers to increase retention in a public care
system.
Specifically, this study involved four sequential phases of research:
1. Explore low-income, minority cancer patient perspectives about depression
treatment, their reasons for dropping out of treatment, and barriers to treatment
adherence;
2. Compare these dropout perspectives and barriers to low-income, minority cancer
patients who complete depression treatment, while also eliciting their enablers of
treatment;
3. Understand the barriers contributing to dropout, from providers’ perspectives;
and
4. Identify viable study provider strategies to decrease dropout and increase
retention to treatment.
The strength of this qualitative study was the use of three types of triangulation
throughout, to enhance the overall rigor and credibility of findings: 1) data triangulation
(the use of a variety of data sources); 2) investigator triangulation (the use of multiple
theoretical interviewers); and 3) theory triangulation (the use of multiple theoretical
perspectives to interpret a single set of data) (Denzin, 1978; Patton, 2002). This study
utilized a grounded theory (Glaser & Strauss, 1967) approach throughout to explore and
46
generate new theory
on the phenomenon of depression treatment dropout for cancer
patients (Creswell & Maietta, 2002). As such, several forms of triangulation were used
as a primary strategy for enhancing the rigor and trustworthiness for this qualitative
research study (Padgett, 1998). Socio-cultural, psychosocial, provider, and health
system factors’ influence on dropout were explored using individual patient interviews,
secondary data analysis of provider notes, and provider face-to-face and telephone
interviews. These inductive methods of data collection elicited patient and provider
perspectives on depression, the utility of depression treatment, barriers to completing a
treatment trial, reasons for dropping out and remaining in a treatment trial, and viable
provider strategies for reducing dropout. Such exploration generated further related
discussion about the experience of depression and cancer, help-seeking behaviors,
treatment experience, stigma, cancer and cancer treatment self-management, family
support and other culturally-mediated coping resources such as religion, and other multi-
level barriers and facilitative factors to cancer and depression (Campbell et al., 2003).
.
As a starting point, this study used conceptual and theoretical models of health
behavior: Meichenbaum & Turk’s (1987) adherence variable categorization, TPB
(Fishbein, 1967; Fishbein & Ajzen, 1975)
and Cultural Explanatory Models (CET)
(Kleinmann et al., 1978), which utilized psychosocial, socio-demographic, illness-related
and cultural factors to explain influence on health behavior decisions, particularly to
depression adherence issues within the context of a study trial. However, rather than
test these theories, the intent was to build theory through the generation of a heuristic
model of patient decision-making with respect to adherence and dropout. Sensitizing
concepts (Blumer, 1954), based on Meichenbaum & Turk’s (1987) adherence variable
categorization (characteristics of the patient, characteristics of the depression treatment
and cancer treatment regimens, features of the disease, health care provider and the
47
patient , and the clinical setting) provided a starting point to explore these barriers and
factors. The TPB (Fishbein, 1967; Fishbein & Ajzen, 1975) was incorporated into the
individual interview guides, which focused on the basic description of a dropout’s
intention for depression treatment, through subjective norms, attitudes, and perceived
behavioral control. This was especially important for the purposes of this study because
this particular group of drop out patient’s initially agreed to participate, but then at some
point in treatment, actively or passively became disengaged. Additionally, each
participant’s explanatory model (Kleinmann et al., 1978)
of the cause (etiology), course
(symptomatology), and cure (appropriate response or treatment) of depression was
elicited to explore the potential role of this model in the decision to withdraw from
depression treatment. There were only retrospective data responses for reasons of
dropout and completion.
ADAPt-C Depression Treatment Model (Parent Grant)
It is important to acknowledge that this smaller dropout study and the ADAPt-C
parent grant study are two distinct studies. This dropout study drew from the patients
who dropped out of the larger ADAPt-C parent grant study and was conducted within the
context of ADAPt-C, which will be described briefly.
The ADAPt-C randomized controlled trial enrolled 472 patients from June 2004 to
December 2006. Blinded outcome interviews for all enrollees were carried out at 6, 12,
18, and 24 months, which were completed in December, 2008. The effectiveness of the
ADAPt-C intervention was assessed by comparisons of PHQ-9 scores assessed at
baseline versus 6, 12 and 18 months follow-up (Thase et al., 2002).
Collaborative depression care interventions included a structured algorithm for
stepped care management and protocol for treating depression, and telephone
maintenance/relapse prevention and outcomes monitoring over 12 months (Ell et al.,
48
2008). The ADAPt-C intervention design was adapted from collaborative care quality
improvement interventions for depression that effectively integrate mental health
professionals into primary care and are effective for low-income and minority patients
(Dwight-Johnson et al., 2005; Ell et al., 2008). These successful collaborative care
interventions address the essential elements of the Chronic Care Model (Wagner et al.,
2005): 1) the community, 2) the health system, 3) self-management support, 4) delivery
system design, 5) decision support, and 6) clinical information systems (Dwight-Johnson
et al., 2005). Intervening at these component levels fosters productive interactions
between patients who take an active part in their care, and providers backed by
resources and expertise. In turn, these interactions promote improved health status,
higher satisfaction for patients and providers, and lowered costs.
Patients were seeking oncology care at LAC + USC Medical Center, a public
sector care system in East Los Angeles, California. The intervention was an
individualized stepped care depression treatment program provided by a Cancer
Depression Clinical Specialist (CDCS) in collaboration with a study psychiatrist. Bilingual
social workers with a Masters degree were chosen to implement the CDCS role because
previous studies with the medical center population found that many patients need
patient navigation and case management services to address barriers to engagement in
depression care (while simultaneously managing their cancer treatment), as well as
supportive assistance in addressing psychosocial and practical problems in their daily
lives. In addition, medical and nursing oncology staff were comfortable with social
workers working with cancer patients, as this model is used throughout the medical
center via the clinical social work department. The social workers carry out the majority
of treatment, communicate with the oncologist and nursing staff as needed, act as
translators during psychiatric evaluations and provide patient navigation/case
49
management services. The initial CDCS visit included extensive patient education, a
semi-structured psychiatric and psychosocial history and assessment, consideration of
initial treatment choice, provision of patient navigation assistance, and in some cases
included meeting with family members. Given that the majority of these patients are
unfamiliar with depression as a concept or its treatment, patient education was
implemented based on CDCS clinical judgment and included discussion of the brochure
provided all patients (adapted from the IMPACT study for this population), and optional
use of a video in Spanish or English on depression. Depression education included the
discussion of etiology (both biochemical and environmental factors), common depressive
symptoms among cancer patients, and the advantages and concerns about counseling
and/or antidepressant treatment. The clinical goal was to increase the likelihood of a
successful outcome to depression treatment by reducing perceptions of stigma and to
empowering patients in taking an active role with their CDCS, psychiatrist, or oncologist
in understanding and deciding on first line treatment. The CDCS also interacted via
written clinical notes or verbally with the treating oncologist.
Based on the known barriers to retention, ADAPt-C implemented participant and
investigator focused strategies that included: socio-cultural adaptations including literacy
and idiomatic sensitivity in written educational materials and homework materials;
integration of depression treatment with cancer care; telephone counseling as an option
at all stages as was family participation with patient agreement reminder phone calls; bi-
lingual and bi-cultural staff; scheduling and location flexibility; weekly therapist
supervision; weekly clinical team conference calls; extending depression counseling
sessions to account for patient barriers; a psycho-education component to counseling;
and phone counseling, if needed during later counseling stages to meet patient needs.
50
Patient Inclusion/ Exclusion Criteria
ADAPt-C parent grant inclusion criteria included adult cancer patients >90 days
from a cancer diagnosis who were receiving acute treatment or follow-up care in
oncology clinics, but did not have advanced cancer or another medical condition that
limited remaining life expectancy to less than 6 months (Thase & Ninan, 2002). The
Patient Health Questionnaire depression scale (PHQ-9) was used because it provides
both a dichotomous diagnosis of major depression as well as a continuous severity
score (Kroenke, Spitzer, & Williams, 2001) and measures a common concept of
depression across racial and ethnic groups (Huang, Chung, Kroenke, Delucchi, &
Spitzer, 2006). If patients met criteria for major depression (one of the 2 cardinal
depression symptoms plus a PHQ-9 score of > 10), an additional screening protocol was
administered to exclude patients with current suicidal ideation, alcoholism, recent use of
lithium or antipsychotic medication and a self-reported Karnofsky Performance Status
Scale score of 2 or less out of 10 points representing severe functional impairment in
cancer patients (Mor, Laliberte, Morris, & Wiermann, 1984). The critical distinction of this
ADAPt-C cancer and depression study was that patients meeting diagnostic criteria for
clinical depression (versus all cancer patients receiving a general preventive approach)
and/or dysthymia were eligible for care in this study.
Psychological treatment is usually operationalized with a specified course of
treatment with a recognized professional (Sharpe et al., 2004), which for this study
included the goal of active PST treatment to last between 8-10 sessions. PST drop outs
were defined as patients who had fewer than 4 PST sessions. PST dropouts included
those who initially agreed to be randomized to the intervention, but thereafter, had either
verbally declined treatment or not shown up for the appointments. This included patients
who had refused some sessions, but agreed to remain in the study for outcome
51
interviews. Dropout exclusion included patients who died or were unable to be reached.
PST patients who had completed treatment were defined by their therapist as
completing treatment, and were thus included in the completer subgroup.
Antidepressant medication completion is usually defined as taking a standard 9
months for major depressive disorder (based on a PHQ-9 score of 10 or more), and up
to 2 years for patients with scores of 20 or more. For this study, patients receiving anti-
depressant medication were dropouts if they discontinued treatment within 30 days
(which, in the literature occurs in 30-40% of patients), which is often the standard
benchmark used for determining adherence to antidepressant medication(Lin et al.,
1995). Discontinuation of treatment is often greatest during the first month and continues
at a slow but steady rate thereafter (Simon, 1993), with more discontinuing
antidepressants by the third month of therapy (Lin et al., 1995). Again, for antidepressant
medication treatment, patients could have refused further medication, but agreed to
complete ADAPt-C parent grant outcome interviews and receive gift card incentives.
Sampling Plan
Based on the qualitative principle of theoretical saturation and sampling (Strauss
& Corbin, 1998), target accrual for individual interviews was 20 dropout participants and
10 completer participants. Rationale for this 2:1 ratio was that dropouts were of greatest
emphasis, thus necessitating more perspectives about declining treatment; Completer
perspectives were used to compare the barriers and enablers of treatment. The
sampling plan was going to continue until the target accrual was met or at the point in
category development, at which no new properties, dimensions or relationships emerged
during the analyses(Strauss & Corbin, 1998). Completer perspectives reached
saturation much sooner (around interview number 6), than dropouts (around interview
number 14).
52
Although the dropout participant sample pool consisted of 21/39 predominately
Spanish-speaking Latino females, an attempt was made to select a stratified purposeful
sample based on gender. Stratified purposive sampling was appropriate because it is a
qualitative method aimed at selection of information-rich cases, strategically and
purposefully, in order to illustrate characteristics of participants and facilitate
comparisons (Patton, 2002). In addition, highly regarded comparisons increase the
internal validity of the findings (Boeije, 2002). Such contrast was important for this study
based on the literature and preliminary recruitment studies, which showed that men have
more difficulty being engaged in depression treatment and are more likely to dropout.
Ultimately, these comparisons will inform the design of gender-based intervention
models which can be culturally tailored to capture the patients’ cultural needs and
preferences. An effort was also made to gain non-Latino participation (in addition to
Latinos), in order to gain a varied sample of dropouts, however due to a predominately
homogenous sample pool of Latinas (female), it was difficult to compare across ethnic
groups with such small numbers of non-Latinos.
Sampling Plan – Phases 1 and 2
Dropout and completer participants were recruited by telephone from clinically
trained bi-lingual recruiters who were working on the parent grant study. Given the
barriers and vulnerability of dropout for this population, an in-depth, semi-structured
telephone interview design was well suited for this particular study and offered
participants with a more practical, convenient alternative to coming into the hospital.
Participants were more likely to agree to participate and felt more at ease and less
pressured at home or in their own environment. In order to strengthen the accuracy of
data, researchers typically conducted more than one interview in order to ensure data
accurately reflects patient perspectives that are being interpreted. There was one initial 1
53
hour telephone interview, followed by a shorter follow-up telephone interview for
clarification, approximately 1 to 2 weeks after the initial interview. Although patients were
given the convenience option of participating at one of the Los Angeles County +
University of Southern California (LAC + USC) Medical Center clinics during their next
clinic appointment, all patients preferred to be interviewed over the telephone.
Participants were mailed their choice of a $25 Food 4 Less grocery store or Target gift
card for their time to complete the qualitative telephone interview. Participants were
additionally compensated with a $10 gift card for their time to complete the 20 to 30
minute follow-up qualitative telephone interview. Participants who were not available by
telephone, with at least 8 attempts at different times of the day and week (based on
ADAPt-C protocol guidelines), were mailed a written study invitation along with a self-
addressed, stamped envelope, requesting that they indicate a convenient time to call
back for the telephone interview.
Completer patients were randomly selected via computer algorithm - by
reordering the completers based on a computer generated number from the uniform
distribution. Then the first 10 from each English-speaking and Spanish-speaking groups
were chosen.
Sampling Plan – Phase 3
Documentation and weekly logs are important to tracking clinical information (Weber
et al., 2004). As with other studies, written clinical records were retained of patients’
reasons for declining to participate (Burton, 1995). Secondary analysis of dropout notes
were used to describe various analytical practices that use pre-existing data either to
investigate new research questions or to re-examine primary study questions for
purposes of corroboration (Heaton, 2008). There was no need to analyze completer
notes, as this study was mainly focused on the perspectives of dropouts. Completers’
54
perspectives were just used to compare the issues and elicit enablers of treatment
completion. Unlike quantitative approaches, the published literature on secondary
analysis of qualitative data is sparse (Corti & Bishop, 2005) and under-developed
(Heaton, 2008). However, secondary data can be an efficient method of describing and
comparing pre-existing data in different contexts. Due to the nature of analysis from
patients who chose to no longer continue with the study, there is somewhat of a dearth
of information from clinical notes, as compared to patients who completed treatment.
Due to the constraints of the slightly different goals for the original study, there is simply
limited in-depth explanation for the reasons for dropout. In order to conduct the
secondary analysis, clinical documentation notes from the dropouts were sampled from
after receiving medical release from dropout participants.
Sampling Plan – Phase 4
ADAPt-C study clinicians (social worker, psychiatrist, psychiatrists) and study
staff (project manager, project assistant, recruiter) appropriately contributed to viable
strategies to decrease dropout, within the context of a depression treatment trial. Such
strategies can also be useful for real world treatment retention treatment and
interventions. In-depth, semi-structured telephone interviews were well suited for busy
providers and study staff because it offered a more practical alternative for coordinating
a convenient time for 16 different provider schedules, coming from 3 different clinic and
hospital settings. Participants were also given the convenience option of participating in
a face-to-face interview at their worksite, and 8 of them choose this option, over the
telephone interview. Telephone and face-to-face interviews were approximately 1 hour.
Personnel from the ADAPt-C study and a related depression trial (Multifaceted Diabetes
and Depression study) under the same Principal Investigator (Kathleen Ell, DSW) and
under the same LAC + USC system were recruited. The Principal Investigator invited
55
each provider via telephone request and then followed up with a detailed email request.
Participants were mailed a $10 Starbucks gift card for their time in completing the
interview.
Data Collection
Data Collection - Phases 1 and 2
The first phase of dropout data collection involved in-depth minimally structured
individual telephone interviews. These questions were related to participation in the
context of a research trial study, not depression treatment. During this phase of data
collection, the Principal Investigator hoped to gain an illustration of factors which led up
to the patient actually declining to come into scheduled sessions, e.g. their decision to
passively discontinue to attend sessions with the therapist or after taking an
antidepressant (prescribed by the study psychiatrist) which caused uncomfortable side
effects. Under the supervision of the Principal Investigator, there were two bi-lingual
project assistants (Maria Hu-Cordova, MSW and Sylvia Barker) who worked on the
ADAPt-C study and were trained to conduct all Spanish-speaking telephone interviews.
The Principal Investigator conducted English speaking interviews. Recruitment included
contacting participants by telephone to request participation. The interviews were
digitally recorded, while the project assistant took notes and summarized both the
process and content of the interview. At the end of the interview, self-report demographic
data were collected from the participant to confirm correct demographic and contact
information for second interview.
The initial dropout interview questionnaire was developed based on cancer-specific
disease and treatment, practical barriers associated with low-income status, and cultural
aspects purported to be of particular relevance within minority or Latino populations. This
interview guide used prompts, minimally based on the TPB. The socio-cultural
56
dimension prompts drew on CET. Although these prompts were used to focus the
participant on their reasons for dropping out of the study, caution was taken to relying
extensively on the prompts and questions. As such, a grounded study was “grounded” in
these participants’ responses. The participant was allowed to tell their story with minimal
interruptions or probes, while addressing all key components of the interview guide.
Probed questions were only to be used to guide the discussion about dropout, but not
intended for the interviewer to ask every question on the interview guide. The trained
interviewer was cautioned about using the word “dropout” during the interview, as this
word had the potential to have negative connotations to the patient and/or might not
accurately reflect the patient’s status according to their individual perspective. Instead,
interviewers were advised to refer to dropout as “discontinuing treatment.” Responses to
the interview guide protocol were obtained through an iterative process
18
, consistent with
the nature of qualitative research (Denzin, 1978).
Table 1 provides a concise representation of the relationship between the TPB
theoretical constructs and definitions, corresponding literature and past studies for this
study population, and the dropout interview script prompts. Taken together, this
representation was useful to systematically organizing how certain findings reflect
related constructs and contribute to a coherent, grounded model of improving adherence
in low-income, minority populations (particularly in Latino patients). The purpose of
theory and previous depression treatment and adherence literature among low-income
and minorities is to theoretically guide an exploration and analysis of treatment dropout.
57
Table 1: Origination of dropout interview script prompt examples and exploration areas
TPB model
constructs
TPB model
construct
definitions, and
meaning in
relation to study
Literature review
excerpts related
to TPB model
constructs
Dropout interview
script prompt
examples
Main areas to
explore for this
study
1.Behavioral
Beliefs
Beliefs that
underlie a person’s
attitude toward
depression
treatment
participation
Ethnic minority
groups
conceptualize
depressive
symptoms as
social problems or
emotional
reactions to
situations, while
white middle class
people are more
apt to view
depression as a
disease requiring
professional
treatment (Karasz,
2005).
Latinos are less
likely than are
Whites to find
antidepressant
medication
acceptable, but are
more likely to find
counseling
acceptable
(Burton, 1995;
Fukui, 2000).
1.What do you
know about
depression and
treatment for
depression?
2.What treatment
do you think works
better for
depression or
doesn’t work well?
3.What have you
heard about
antidepressant
medication?
4.Do you think
talking to a
professional or
taking medication
for depression?
Perceptions of Depression
and
Depression Treatment
2.Outcome
Evaluations
Beliefs about the
consequence of
participating or not
in depression
treatment
1.What happens to
people with
depression if it
does not get
treated by a
professional?
2.What would
happen if someone
you knew did or did
not get treatment
for depression?
Would they get
better, worse, or
stay the same?
3.Attitude Overall evaluation
of completing
depression
treatment
3.What type of
treatment do you
feel is ‘better or
good’ - counseling
or anti-depressant
medication or
both? And why?
4.What type of
treatment do you
feel is ‘worse or
bad’ - counseling
or anti-depressant
medication or
both?
58
Table 1, Continued
TPB model
constructs
TPB model
construct
definitions, and
meaning in
relation to study
Literature review
excerpts related
to TPB model
constructs
Dropout interview
script prompt
examples
Main areas to
explore for this
study
4.Normative
beliefs
Beliefs concerned
with the likelihood
that significant
others approve or
disapprove of
depression
treatment
We do know that
family perceptions,
may also impede
receipt of care
among low-income
and minority
populations
(McClure et al.,
1999).
1.What people (in
or outside of your
family) would you
most likely listen to
and act on if they
said that if was not
a good idea to start
or stop depression
treatment?
Role of Support
5.Motivation to
comply
The persons
tendency to accept
the directives of
certain groups of
people or
individuals
1.How do these
people and those
around you
influence your
decisions about
depression
treatment and your
health and mental
health?
2.How did these
people influence
your decision to
participate in
depression
treatment? And
how do these
people influence
your decision to
discontinue with
depression
treatment?
6.Subjective
Norm
Perceptions of
social norms and
pressures to
discontinue with
depression
treatment
1. In what ways do
people close to you
influence your
decisions about
getting depression
treatment and/or
deciding to
discontinue
treatment?”
2. How would your
decision change or
not if your doctor
recommended that
you go to see a
therapist or
psychiatrist for
depression
treatment, versus
someone close to
you?
59
Table 1, Continued
TPB model
constructs
TPB model
construct
definitions, and
meaning in
relation to study
Literature review
excerpts related
to TPB model
constructs
Dropout interview
script prompt
examples
Main areas to
explore for this
study
7.Control beliefs Beliefs regarding
barriers and
enablers to
continuing
depression
treatment
Perceived practical
barriers such as
cost and
transportation to
therapy may also
impede receipt of
care among low-
income and
minority
populations
(McClure et al.,
1999).
Barriers are
often common to
all underserved
people (i.e. lack of
time,
transportation,
neighborhood
safety, or
economic
resources) (Van
Duyn et al., 2007).
Two of
the mostly
commonly reported
practical patient-
level barriers to
depression
care were child
care or other family
responsibilities and
transportation
problems (Dwight-
Johnson et al.,
2005; Van Duyn et
al., 2007), related
to time and
distance concerns
(Sharpe et al.,
2004).
1.What are some
barriers that got in
the way of
continuing your
depression
treatment with the
therapist or
psychiatrist?
2.What are some
things that might
have helped you
remain in
depression
treatment?
Barriers/ Enablers
8.Perceived
power
Level of confidence
the person has,
that the belief
about their
enablers and
barriers do indeed
facilitate or inhibit
performance of the
behavior
1. Despite these
treatment barriers,
how confident are
you about getting
help for your
depression if you
need to in the
future?
2.How motivated
are you to get
depression
treatment? If so,
why?
9.Perceived
behavioral
control
Beliefs regarding
how easy or
difficult it is to
participate and
complete
depression
treatment
1.How easy or
difficult is it for you
to follow through
with depression
treatment or to see
a psychiatrist or
therapist?”
10.Behavioral
Intention
Perceived
likelihood or
subjective
probability that the
person performs
the behavior
(depression
treatment)
11.Behavior Non-/ Participation
in treatment
60
Immediately after the initial interview, the interviewer filled out a Telephone
Interview Debriefing/ Feedback Form (informed by Deborah Padgett, PhD) (Appendix 1
includes the English version and Appendix 2 includes the Spanish version), which
included questions about the interviewer’s impressions, such as the patient’s demeanor
and mood during the telephone interview (seemed to be anxious, impatient, relaxed,
angry); major issues for the participant; a brief self-reflective critique of the interviewer
herself; recommendations for a follow-up interview; and areas for further exploration.
After the interviewer received the completed transcription (within 1 to 2 weeks), she
reviewed it for accuracy, making note of line numbers where corrections needed to be
made on the transcript. This follow-up interview took between 20 to 30 minutes and
involved primarily these central questions: “you have indicated …., is this correct?” And
“is there something else you would like to add?” This second interview, described as
member checking (Lincoln & Guba, 1985), is a qualitative process which decreases
researcher bias and involves returning back to the field periodically to ensure that one is
on the right track with previously collected data (Padgett, 1998).
The dropout interview guide was minimally organized to address the following
issues: (1) patient definition of dropout, which included examples of the following
prompts: “Some people would describe your not wanting to be in treatment anymore as
“dropping out?”; “Is this how you would consider it for your situation?”; “What word(s)
would you use to describe ‘declining’ or ‘dropping out’ of treatment?; (2) reasons for
dropout, which would include an example of the following prompt: “What are the
circumstances you discontinued your depression treatment?”; (3) subjective norm from
the TPB construct and CET (which include normative beliefs and motivation to comply)
is the decision to accept or not to accept a health behavior based on environmental
influences (i.e. family, friends, doctors, etc.), and would include examples of the
61
following prompts: “In what ways do people close to you influence your decisions about
getting depression treatment and/or deciding to discontinue treatment?”; “How would
your decision change or not if your doctor recommended that you go to see a therapist
or psychiatrist for depression treatment, versus someone close to you?; (4) attitude from
the TPB construct (which includes behavioral belief and outcome evaluation) is the
attitude, values, beliefs, myths, and misconceptions of favorableness or unfavorableness
toward a health behavior, and would include examples of the following prompts: “What
type of treatment do you feel is ‘better or good’ - counseling or anti-depressant
medication or both? And why?”; “What type of treatment do you feel is ‘worse or bad’ -
counseling or anti-depressant medication or both?; and (5) perceived behavior control
from the TPB construct (which includes control beliefs and perceived power) are beliefs
regarding how easy or difficult it is to follow through with depression treatment, which
includes resources, opportunities, obstacles, impediments, and confidence in acting on
or not acting on a particular health behavior. Examples of prompts included: “How easy
or difficult is it for you to follow through with depression treatment or to see a psychiatrist
or therapist?”; “What are some barriers that might get in the way of following through
with depression treatment or attending treatment sessions with the therapist or
psychiatrist?”; “What are some things which might help you with staying in counseling or
on anti-depressant treatment for depression?”
With regard to the completer interviews, 10 randomly selected ADAPt-C
depression intervention completers were interviewed using an in-depth, semi-structured
interview guide. A grounded theory qualitative methodological approach was again
employed, but instead to explore their perspectives and enablers of depression
treatment completion. Interview guides were developed by again incorporating
sensitizing concepts from existing adherence literature and theoretical frameworks (e.g.
62
TPB, and Cultural Explanatory Model). Just as the dropout interview guide, the
completer interview guide was also minimally organized to address most of the same
issues included in the dropout interviews: (1) Reasons for remaining in treatment; (2)
TPB subjective norm construct and CET concepts of normative beliefs and motivation to
complete; (3) TPB attitude construct; (4) TPB perceived behavior control; and (5) “things
that often made it difficult for them to attend treatment” (or barriers which often
interfered with treatment). The main areas to explore for the dropout and completer
phases of this study included: 1) barriers or enablers to depression treatment, 2)
perceptions of depression treatment, and 3) the role of support.
Data Collection - Phase 3
The second phase of data collection involved the review of abstracted secondary
qualitative data from the ADAPt-C case manager, psychiatrist, and clinical social work
clinical notes in the ADAPt-C study’s tracking and reporting web-site. During this phase
of data collection, the Principal Investigator hoped to identify a comprehensive list of
psychosocial factors which contributed to dropout. Sensitizing concepts from
Meichenbaum & Turk’s (1987) Adherence Framework (Andersen & Newman, 1973)
were useful in organizing the abstracted data and describing the experience of patient
dropout from the perspective of the services provider. This was important for the reason
that many times provider judgment and definition is used as a means of defining drop-
out (Acosta, 1980; Aday & Andersen, 1974; Pekarik, 1992), which may be different than
that of the patient. It is important here to gain the reason for dropout from an additional
perspective, that of the provider. Clinician accounts of patient behavior, patient-provider
interaction, responsiveness to treatment, and other relevant factors that may influence
each of these three variables (e.g. cultural beliefs, concerns about adherence, reports of
side effects, experience of stressful life events external to cancer treatment) were
63
identified and coded. Such factors were used for subsequent interviews with providers to
confirm and stimulate discussion.
Data Collection - Phase 4
Interviews were expected to elicit a wide range of clinical ideas, strategies, and
institutional barriers and solutions for addressing the problem of dropout in this context.
The Principal Investigator conducted all interviews with providers. Interview protocol
questions (in Appendix 3) were based on what was learned from the interviews and
provider notes, with regard to the reasons that patient’s dropout. Additionally, interviews
were also guided by sensitizing concepts from the literature which included dropout
factors and reasons which focus on patient-provider interpersonal factors and
motivation; feasible retention strategies (letters and/or phone calls and flexibility), and
intervention implementation challenges and barriers that they face in a large public care
system medical center. These topic areas were to be used as prompts for discussion,
with the goal of gaining a better understanding of dropout and strategies for retention
from the provider’s perspective. Although the predetermined probes, based on
sensitizing concepts, were used to guide the discussion, all relevant opinions related to
systems and organizational resources and constraints relating to the dissemination and
implementation of sensitizing concepts were elicited, allowing the provider to present
their own model of the barriers and issues.
Data Analysis
Data Analysis - Phases 1 and 2
During the first phase of data analysis, the Principal Investigator hoped to find the
extent to which cultural attitudes and beliefs about depression treatment played a part in
patient’s decisions to either drop out of treatment or complete treatment; and in what
ways socio-demographic barriers influenced their practical daily life priorities. Both
64
dropout and completer interviews were audio recorded for accuracy. Using a
methodology of “Coding Consensus, Co-occurrence, and Comparison” (Williams, Best,
Taylor, Gilbert, & al, 1990) and rooted in grounded theory (Glaser & Strauss, 1967), all
transcripts were analyzed using Atlas-ti (which is a qualitative software system that will
be used to examine data) in the following manner. Empirical material contained in the
interviews were independently coded by writer at a very general level in order to
condense the data into analyzable units. Segments of transcripts were assigned codes
based on a priori TPB sensitizing concepts (i.e., questions in the interview guide) with
emphasis on CET or cultural explanatory model of illness, or emergent (e.g. issues
raised by the respondents themselves) themes (also known as open coding) (Strauss &
Corbin, 1998). In many instances, the same text segment was assigned more than one
code. Following the open coding, codes were assigned to describe connections between
categories and between categories and subcategories (also known as axial coding
(Strauss & Corbin, 1998). During this process, short descriptive memos were prepared
to document all investigators’ initial impressions of the topics and themes and their
relationships and to define the boundaries of the specific codes (Miles & Huberman,
1994). Under the supervision of advisors, each transcript was independently coded by
writer and the 2 project assistants. Disagreements in assignment or description of codes
were resolved through discussion between investigators to enhance or refine code
definitions. The final list of codes, constructed through a consensus of team members,
consisted of a numbered list of themes, issues correlated with dropout, accounts of
behaviors, and opinions that related to specific socio-cultural barriers that influenced
depression treatment dropout. With the final coding structure, three investigators
separately reviewed a minimum of 6-7 transcripts to determine the level of agreement in
the codes applied. Based on these codes, the computer program Atlas-ti was used to
65
generate a series of categories arranged in a treelike structure connecting transcript
segments grouped into separate categories or “nodes.” These nodes and trees were
used to examine the association between different a priori and emergent categories and
to identify the existence of new, previously unrecognized categories. The number of
times these categories occurred together, either as duplicate codes assigned to the
same text or as codes assigned to adjacent texts in the same conversation, was
recorded, and specific examples of co-occurrence illustrated with transcript texts.
Through the process of comparing these categories with each other, the different
categories were further condensed into broad themes using a format that places
perspectives about depression treatment in general and dropout factors within the
framework of LAC + USC’s organizational and systems characteristics (Glaser &
Strauss, 1967).
For each interview, demographic characteristic, a matrix of categories were
constructed which placed dropout factors on one dimension and culturally-influenced
beliefs and behaviors related to dropout on the other dimension. Such matrices were
used to identify the context of the local culture as it related to depression treatment
dropout. Comparison of matrices across groups (by noting categories or themes
common to more than one group and themes unique to the specific group) (Boeije,
2002) enabled the Principal Investigator and advisors to identify the extent of
convergence and sources of variation within the target communities as to perceptions of
barriers and behaviors related for depression treatment dropout decision.
Data Analysis - Phase 3
The third phase of this analysis was a secondary analysis of provider clinical
notes. This analysis was intended to identify perspectives of the patient dropout
experience that patients and providers have in common (triangulation) as well as
66
perspectives that are unique to the provider (complementarity). A template analysis
approach (King & Ross, 2003) was used to identify the general issues related to dropout,
based on a priori sensitizing concepts from Meichenbaum & Turk’s (1987) adherence
variable categorization. However, the specific nature of the categories and themes of the
12 abstracted dropout clinical provider notes were explored and predetermined. Text
from notes were entered into Atlas-ti. Dropout themes were identified through close
reading of the text and then organized into a coding template, which initially produced
the basis of summaries of the main issues apparent in the data (Williams et al., 1990)
from the Principal Investigator and mentors. This product was modified in response to
careful reading and re-reading of the transcripts, until the final template was developed
(King & Ross, 2003; Williams et al., 1990). This method helped the Principal Investigator
gain insight and direct further inquiry during the fourth phase of analysis – provider
interviews.
Data Analysis - Phase 4
The fourth phase of this analysis involved either face-to-face or telephone
interviews, whereby findings from the patient and provider perspectives of dropout (the
first three phases of this study) were discussed in order to develop concrete strategies
for decreasing dropout and identifying barriers to implementing such suggestions. During
this phase of data collection, the Principal Investigator hoped to identify specific provider
techniques, e.g. informal pre-therapy engagement strategies, motivational interventions,
telephone counseling, or reminder phone calls which can be used by providers to reduce
the likelihood of dropout. The final template from the secondary data analysis was used
for provider interviews as a way to often begin the discussion, verify results, and add to
the list of template themes. Again, a grounded theory analytic approach was used to
explore feasible strategies and barriers to engagement and retention of depressed
67
cancer patients to treatment. Provider interviews were coded based on the a priori
retention strategies derived from the literature, which include culturally sensitive
engagement strategies, motivational interviewing techniques, and practical case
management or patient navigation strategies (i.e. letter and phone calls and flexible
scheduling), and barriers which included patient and provider interpersonal relationship
problems (i.e. poor patient satisfaction and a disengaged therapeutic alliance). Empirical
materials contained in the provider interviews were independently coded by the Principal
Investigator at a very general level in order to condense the data into analyzable units.
Segments of transcripts were assigned codes based on a priori sensitizing concepts
about retention and engagement strategies and barriers to implementing, with an
emphasis on working in a public care system, in addition to the emergent categories that
arose from the providers themselves.
Products from 4 Phases of Data Analysis
Three distinct products resulted from the analysis of the qualitative components
of this grounded theory design. First, understanding of the patient experience was
accomplished by using their own words to create a “thick description” (Geertz, 1973)
of
patient cultural explanatory models of the nature of dropout; perceptions of depression
and depression treatment, in the context of having cancer, and perceived cancer and
depression care needs. This thick description included a set of themes organized into
discrete categories with each theme illustrated by one or more specific examples or
“case studies” that highlighted instances where identified socio-cultural factors may have
facilitated dropout or hindered the delivery depression treatment. Second, more insight
was gained about these dropout experiences, through the systematic comparison of
barrier and enabler frequencies. Third, a preliminary model of explanatory factors that
may play a critical role in depression treatment dropout among cancer patients was the
68
product of the secondary data analysis. In addition, a list of reasons and factors (from a
provider’s perspective) leading to dropout confirmed and complemented the perspective
of the patients obtained in Phase I, thereby increasing confidence in the generalizability
of the preliminary model when applied to a low-income, minority population. A
preliminary model of explanatory factors that played a critical role in program
participation and implementation was identified to inform sustainability efforts, based on
an organized list of personal and system characteristics of the clinic (e.g., identified by
more than one source). And fourth, based on the patterns established and the lessons
learned from providers, the goal was to obtain a specified list of feasible, practical
engagement and retention strategies which can be used to decrease dropout among
low-income, minority depressed cancer and chronically ill patients being treated in a
public care system. A list of barriers with congruent implementation strategies can be
used to inform institutional and organizational program improvement and policy
decisions. This component made a unique and novel contribution to the parent grant, by
identifying feasible strategies that these providers can use directly with ADAPt-C and
other similar patient populations to decrease treatment dropout in the context of a trial or
in real-world treatment.
Protection of Human Subjects
Human subjects risk issues were addressed in IRB and throughout the course of
the study. The protection of human subjects’ education and HIPAA courses were taken.
Interviewers were trained and supervised by the Principal Investigator on conducting the
telephone interview, verbal consent, and suicide risk protocol. The waiver of informed
consent was included in the interview guide introduction to request that the clinical
records of the subjects for the third phase of the study would be allowed. Recruitment
protocols, interview guides, and debriefing forms were translated in Spanish and used by
69
bi-lingual interviewers. All study materials were written in simple English or Spanish and
efforts will be made to avoid technical language or jargon to allow for full participation by
subjects with lower levels of comprehension or literacy. The consent process took
approximately 10 minutes to complete. In order to help ensure that subjects adequately
understood all of the information presented, the interviewer asked subjects to summarize
the information, including the purpose and procedures of the study as well as the
potential risks, benefits, and alternatives to participation. The interviewer was prepared
to answer any questions and concerns about the study. The interviewer mailed the
participant a copy of the consent form for review and discussion with significant family or
friends, if they chose.
It is important to note that the recruitment materials and scripts were sensitive to
and addressed that these individuals decided against continuing in a prior research
study. It was possible that they could continue to decline to participate in this smaller
research study as well. The materials made it unequivocally clear that they are being
invited to participate in a research study because they had not continued in a prior
research study.
Anticipated Benefits Relative to Risks
In previous studies of cancer patients, distress resulting from interviews and
questionnaires had been minimal. However, during the interview there was the chance
of psychological distress related to the in-depth nature of the interview and the
expression of feelings and thoughts about their cancer diagnosis and depression.
Although there were no adverse events during the study, the Principal Investigator and
study investigators were aware that participants could experience inconvenience and
time costs associated with completing the in-depth interview. It was made clear that the
interview was completely voluntary and would be conducted at the patients’
70
convenience. Participants were advised that they were free not to answer any questions
that made them uncomfortable. In the event of adverse effects or unexpected results,
this investigator would have reported it to the IRB as required by IRB regulations. Project
staff were expected to directly report such events as they occurred.
Procedures for Protecting Against Potential Risks
The Principal Investigator trained interviewers using the parent grant ADAPt-C
Suicide Risk Protocol in the event that a potentially suicidal patient was identified. This
investigator or another Licensed Clinical Social Worker, would be contacted by cell
phone or pager immediately in the event that the patient became suicidal during the
interview process. Another trained licensed depression care specialist (psychiatrist or
licensed clinical social worker) would provide clinical backup to the Principal Investigator.
Inclusion of Women, Minorities, and Children
All participants in the parent grant and this study were to be > 18 years old. Of
those dropouts and completers who participated in the study, there were female, male,
Latino, African American, Asian, and Caucasian.
Data and Safety Monitoring Plan
Appropriate measures were taken to assure confidentiality of all study records. A
centralized database was kept within the Dr. Ell’s research suite at the Hamovitch
Research Center in the USC School of Social Work. All study participants were identified
by a unique study code number, the identity of which was be known only to the study
personnel with direct responsibility for gaining patient data. The study database was
password protected against non-project personnel and backed up weekly. Data were
stored in a locked file cabinet in the research office of Dr. Ell. Hardcopy study data will
be kept in the secure location in the USC School of Social Work for a period of 7 years
following study completion, after which it will be destroyed.
71
Chapter 4: Sample Description
Phase 1 and 2 Recruitment Details
Initial phase 1 dropout interviews were conducted with all 20 participants in
January and February, 2008 and follow-up dropout telephone interviews were conducted
between late January and mid-March, 2008. Completer interviews were conducted in
February and March, 2008. Of the 20 dropouts, the majority chose a Food 4 Less gift
card for both the initial interview (n=18) and the follow-up interview (n=17) incentives,
because this store was to be more convenient in all cases. One of the dropouts
who originally chose a Food 4 Less gift card for the initial interview incentive, opted for a
Target gift card at the follow-up interview. The other two preferred a Target gift card at
both the initial and follow-up interviews because one doesn’t shop at Food 4 Less and
the other one lived in Bakersfield, where there were no Food 4 Less stores close-by.
More than half of the 10 completers preferred a Food 4 Less gift card (n=6) as an
incentive for their participation in the telephone interview.
Figure 1 represents the development of this study’s dropout recruitment efforts,
which occurred between early January and mid-March, 2008. At the time of recruitment
for this smaller retention study, there were 39 parent study dropouts who initially
completed the baseline assessment and were randomized to the intervention. Of these
39 participants, 19 were still not able to participate for the following reasons: moved
(n=1), disconnected telephone number (n=5), changed telephone number (n=1), unable
to be reached by telephone (n=10), or died (n=2). However, of the remaining 20
dropouts who were able to be reached, all were willing to participate. Of the 10
intervention completers who were initially contacted, all 10 also agreed to participate in
the study. Thus, a total of 20 dropout and 10 completer participants were included in the
final analysis.
72
Figure 1: Dropout Recruitment Details.
Interview Details and Subtleties
Interviewing details are always important to consider. In this study, patient
demeanor (distinguished by patient’s telephone interview behavior and conduct) and
mood (distinguished by patient’s temperament and disposition during the telephone
interview) characteristics were documented on all initial interviews with the 20 dropout
patients. Although dropout interviews were conducted over the telephone, there was
sufficient dialogue to assess each patient’s demeanor and mood, to the extent that this
is possible over the phone. The respondent’s mood during the interview seemed to
affect the ease and flow of the interview. This P.I. (who conducted the English-speaking
interviews) and Spanish interviewer (Maria Hu-Cordova, MSW) reported (according to
whatever terms came to mind, not from a pre-existing list of possible moods), a full
spectrum of dropout demeanors and moods during the telephone interviews. Interview
tone based on patient mood was subsequently defined as “easy” or “challenging.” “Easy”
interviews were characterized as those in which patients seemed to be “happy,”
73
“uplifted,” “laughing,” “upbeat,” “positive” or “optimistic,” “spiritual” or “religious,”
“content,” “pleasant,” “patient,” “relaxed,” and “calm.” Patients in the “easy” interviews
were “very talkative,” “responsive,” and receptive in their “willingness to answer
questions.” Their responses were described as “informative,” “detailed,” “focused,” and
“descriptive” in answering questions. Most of these interviews reflected an underlying
consistency and congruency in mood and demeanor.
“Challenging” interviews were characterized by patients who seemed to be
disengaged, less talkative, rushed or in a “hurry,” “preoccupied,” “agitated,” “impatient,”
“angry,” “unmotivated,” “anxious,” or “seemed to be depressed.” These patients were
reluctant, at least initially. Challenging patients seemed apprehensive and skeptical at
the beginning of the interview, but then became more engaged as the interview
progressed and rapport was developed. Some of the interview challenges may have
been due less to patient characteristics and more to environmental noise and activity, as
one was noted as being somewhat “preoccupied” at times with the interview, when she
was taking care of her children.
The use of interview debriefing forms in the design of this study allowed for the
initial interviewer to designate follow-up interview recommendations and critical
clarification and exploration questions. In addition, the debriefing form allowed the initial
interviewer to communicate sensitive topics and questions about “dropping out” to the
member check interviewer (Sylvia Barker, project assistant). The debriefing form also
required the initial interviewer to critique herself as an interviewer. Interviewer self-
assessment revealed some common interviewer behaviors, particularly with the
“challenging” interviews. These included interrupting the patient, overly explaining
questions, trying to complete patient sentences, and insufficient probing. The interviewer
74
often described herself as feeling somewhat anxious and nervous with the inability to
motivate challenging patients.
Data also reveal instances of interviewers trying to engage and motivate
“challenging” patients to complete the interviews. Interviewers successfully kept
“challenging” dropouts engaged in telephone interviews by offering gentle, motivating
comments when they expressed reluctance to continue. With these interviewing
strategies, all patients who agreed to participate in the interviews completed all
questions within 30 to 50 minutes.
Description of Phases 1 and 2 Samples.
Table 2 represents the demographic, depression, and cancer characteristics of
this smaller study. All dropout and completer characteristics were similar to the overall
parent grant characteristics. In both dropout and completer groups, there were
predominately female (90% of dropouts and 80% of completers), Latino (85% of
dropouts and 90% of completers), foreign-born (80% of both dropouts and completers),
unmarried (55% of dropouts and 70% of completers), and unemployed patients (85% of
dropouts and 60% of completers). More dropout patients were 50+ years old (65% of
dropouts and 50% of completers) and in the U.S. 10+ years (80% of dropouts and 50%
of completers). More completers had less than a 12th grade education (70% of
completers and 40% dropouts). With regard to depression characteristics in both groups,
although most patients had moderate depression levels (80% of dropouts and 60% of
completers), both groups had lower levels of anxiety (10% of dropouts and 20% of
completers). With regard to cancer characteristics, most were diagnosed with a less
advanced cancer stage (70% of dropouts and 80% of completers) and were in follow-up
cancer treatment (70% of dropouts and 60% of completers). There were few in both
groups who experienced severe pain, with only 20% of dropouts and none of completers
75
describing pain. Interestingly, more completers complained of co-morbid medical
problems (70% of completers and 45% of dropouts).
Overall ADAPt-C depression treatment intervention participation of 242 patients
was completed within 3 ½ years. The enrolled intervention patients were predominately
Latina, Spanish-speaking, and foreign born (Ell, K., Xie, B., & Quon, B, 2008). Overall
treated rate was 72.3% (175/242), with 5.7% (10/175) receiving antidepressant
medication only, 53.7% (94/175) receiving PST counseling, and 40.6% (71/175)
receiving both antidepressant medication and PST counseling (Ell, K., Xie, B., & Quon,
B, 2008). There were 27.7% (67/242) of intervention patients who either did not see the
therapist (n=38) or did not continue with further treatment after initial CDCS assessment
(n=29) due to death, declining treatment, leaving the LAC + USC Medical Center or
inability to locate (Ell, K., Xie, B., & Quon, B, 2008).
There were no dropouts receiving medication only or a combination of
medication and PST. There were 10 dropouts who chose PST only and 10 dropouts who
had not yet made a decision or choice about treatment type. No dropouts had completed
4+ total PST treatment and/or 30 days anti-depressant medication visits; and all
completers finished depression treatment (7 PST counseling only and 3 both PST and
medication) sessions. It is important to note that, of the 10 completers, none were
receiving antidepressant meds only. Of the 3 patients who were taking both PST and
antidepressant medication, 2 patients reported continuing meds > 9 months (based on
12 month outcome interviews) and had a PHQ-9 score initially between 10 and 20. The
third completer who received both treatments, also had an initial PHQ-9 score between
10 and 20 and reported taking antidepressant medication for at least 6 months (during
her last PST session), but reported not taking medication at her 12-month outcome
interview. Given that it is unknown if this patient stopped taking medication < or > 9
76
months, it is appropriate to include this patient as a completer due to her PST
completion.
Table 2: Dropout and Completer Demographic, Depression, and cancer Characteristics
Phase 3 Recruitment Details
Figure 2 represents the medical release form recruitment efforts so that parent
grant clinical documentation notes could be abstracted and analyzed. There were 13/20
patients who returned medical release without a second request, reminder or additional
incentive. Two patients returned release only after multiple requests to mail it in, and
being offered another $5 incentive. There were three patients who actively refused to
return due to concerns about “accessing medical records” (n=1), “identity theft” (n=1),
Demographic
Characteristics:
Dropouts (N=20) Completers (N=10)
Female 18 (90%) 8 (80%)
Latino 17 (85%) (2 Black, 1 White) 9 (90%) (1 White)
50+ years old 13 (65%) 5 (50%)
Foreign-born 16 (80%) 8 (80%)
In US 10+ years 16 (80%) 5 (50%)
Unmarried 11 (55%) 7 (70%)
Unemployed 17 (85%) 6 (60%)
Education Level <12 8 (40%) 7 (70%)
Depression Characteristics:
Mild Depression 1 (5%) 0 (0%)
Moderate Depression 16 (80%) 6 (60%)
Major Depression 1 (5%) 3 (30%)
Severe Depression 2 (10%) 1 (10%)
Anxiety 2 (10%) 2 (20%)
4+ Total PST
Treatment and/or > 30
days anti-depressant
medication visits
0 (0%) 10 (100%)
Cancer Characteristics:
Cancer Stage (0,1,2) 14 (70%) 8 (80%)
Cancer Treatment:
Follow-up Treatment 14 (70%) 6 (60%)
Acute Treatment 5 (25%) 3 (30%)
Not yet began
Treatment
1 (5%) 1 (10%)
Pain 4 (20%) 0 (0%)
Medical Co-morbidity 9 (45%) 7 (70%)
77
and concerns about “confidentiality” (n=1). However, one of the patients who was
concerned with “accessing medical records” surprisingly returned her medical release
and then was mailed the $5 extra incentive. Despite recruitment efforts to get the
medical release returned, there were “passive refusers” (n=3), who said they would
return it, but after numerous attempts to contact them by telephone and two reminder
letters, still did not return it.
78
Figure 2: Phase 3 Medical Release Form Recruitment Details.
Dropouts who participated in Phase 1 telephone interviews (n=20)
Dropouts who agreed
and returned medical
release form on 1
st
request (n=13)
Dropouts who agreed to return release, but
required multiple reminder attempts and additional
$5 incentive (n=7)
Returned release
after offered
another incentive
(n=1)
Verbalized and
expressed reluctance
to return medical
release (n=3)
Phase 2 Participating
dropouts (n=15)
Concerned
about
“identity
theft” (n=1)
Concerned
about
“accessing
medical
records” (n=1)
Concerned
about
“confidentiality”
(n=1)
Dropouts who
“actively refused” to
return medical
release (n=2)
“Passive
refusal”
(n=3)
Phase 2 Non-participating
dropouts (n=5)
79
Phase 4 Description of the Providers
Fourteen provider personnel, representing the various care roles in a randomized
depression clinical trial intervention, were interviewed from mid-May, 2008 to early June,
2008. Of the 16 providers contacted to request participation, a total of 14 providers (six
social work therapists, three project recruiters, two patient navigators, one psychiatrists,
one project manager, and one project assistant) responded and were able to conduct
the interviews. Most of the interviews took place in person (n=8) and by telephone (n=5).
However, the psychiatrist providers chose to conduct the interview in writing, as she had
continuous daily clinic appointments and meetings scheduled, which made it difficult to
schedule either an in-person or telephone interview.
80
Chapter 5: Results
This chapter focuses on the findings from the four sequential study research
aims, which were grounded in the literature, previous studies, and theory. The first aim
was to explore low-income, minority cancer patient perspectives about depression
treatment, their reasons for dropping out of treatment, and barriers to treatment
adherence. Major findings indicated that patients who dropped out described multiple
confounding barriers which interfered with continuation of depression treatment: cancer-
related, depression treatment-related, informational, instrumental, cultural, and systems-
related. The second aim was to compare these dropout patients to low-income, minority
cancer patients who completed treatment (“completers”). Completers described fewer
barriers to depression treatment. The third aim was to understand providers’
perspectives on patient dropout. The fourth aim was to identify specific provider
strategies for increasing engagement and retention among low-income minority cancer
patients with depression. Findings indicate that providers can identify feasible strategies
that address some of the various patient-identified barriers. Below, findings related to
each aim will be explored in detail.
Aim 1: Patients’ Perspectives on Their Reasons for Dropping Out and Barriers to
Treatment Adherence
To explore low-income, minority cancer patient perspectives about depression
treatment, their reasons for dropping out of treatment, and the barriers to treatment
adherence, corresponding interview texts were examined. As discussed in chapter 3,
primary exploration areas included: 1) dropout barriers to completion and reasons, 2)
perceptions of depression treatment; and 3) the role of support in treatment decision-
making. It was also important to examine the patient definition and meaning of “dropout.”
81
Patient Definition and Meaning of “Dropout”
It is important to look first at the term “dropout,” to gain insight into how patients
conceptualized the act of terminating their participation. Interviews with dropouts began
with an inquiry about their definition and description of the term. Most patients who
agreed that they had dropped out of treatment were able to provide solid reasons and
circumstances to events leading up to them dropping out of treatment. These patient
interviews were characterized by definitive statements such as, “I decided to stop the
treatment.” However, there were patients who disagreed that they had dropped out of
treatment. For example, one participant said, “Did I drop out? No, I didn’t drop out. I
became busy and I figured I start missing calls.” Similarly, another said, “Like I have
repeated, I didn't stop any treatment.” Another participant expressed that she had not
chosen to drop out so therefore was not comfortable with this label: “It wasn’t dropping
out because I didn’t not want to get help…It’s not like it was under my power. You know
what I mean?...You can say [drop out] if you want to but I don’t feel like I want[ed] to get
out of the study.” Similarly, another participant thought the label sounded negative: “I
think that I dropped out but I don’t know – it does not sound good.” Some participants did
not understand what was being asked and called the question “weird”; one participant
said that her continuation with other treatment meant that she had not dropped out. One
participant said, “I don’t know what to tell you. I’m confused. I can’t answer that question.
You can ask me a thousand times. I can’t answer it.” Despite these findings that “drop-
out” is not a neutral concept and that some patients did not feel that they should be
categorized in this way, the sample remained the same by virtue of the fact that they did
discontinue treatment. The conceptual and clinical issues related to these findings will be
discussed in Chapter 6.
82
Dropout Barriers to Completion and Reasons
Patients identified a number of barriers that seemed to contribute to their
discontinuation of treatment: Cancer-related barriers (e.g. 1] emotional reactions to
cancer diagnosis and 2] competing cancer treatment commitments), depression
treatment barrier (e.g. treatment dissatisfaction), informational barrier (e.g. study
misunderstandings), instrumental barriers (e.g. 1] transportation problems, 2] financial/
medical health insurance, 3] employment, 4] caregiving), cultural barriers (e.g. 1]
language and 2] discrimination), and systems barriers (e.g. 1] patient – provider and 2]
service-related issues). Patients also described the combination of these barriers
simultaneously, in an overlapping manner (e.g. “multiple confounding barriers”). Figure 3
depicts this representation of the various barriers. Before describing the ways in which
barriers interpenetrate for these participants, each cluster of barriers will be briefly
characterized.
Figure 3: Dropout barriers and reasons
83
84
Cancer-related Barriers
Cancer-related barriers included 1) the range of emotional reactions related to
the cancer diagnosis, and 2) the competing commitments involved with their cancer
treatments. Both sets of barriers influenced decisions to drop out of treatment. In many
cases, the cancer diagnosis was compounded by one or more concurrent medical
conditions such as high blood pressure and diabetes. It was not clear in these interviews
the extent to which these co-morbid conditions affected decisions to terminate
participation, but participants did discuss these conditions within their narratives about
cancer-related barriers. This issue of co-morbidity will be discussed further in Chapter 6.
Emotional reactions to cancer diagnosis
All dropout patients (n= 20) discussed the impact and severity of their emotional
reactions to their cancer diagnosis, and the impact of these reactions on their decisions
to discontinue treatment. For example, one Latina woman said, “It was just too much
stress for me every time I would go [so] like I just decided not to go no more…I just didn’t
want to know nothing at that moment. I just want to be like in my own little world …I just
wanted to be by myself. I just wanted to be asleep. I didn’t want to go out nowhere. I
felt scared.” Several participants described being depressed about their diagnosis: a
Latina woman said, “I think [I felt] a little bit of everything—a little bit depressed and a
little bit confused more than anything else…Because I didn't know exactly what was
going to happen and how to take it.”
Competing cancer treatment commitments
Some dropout patients (n=11) also faced challenges due to the multiple
treatments they needed to obtain. Often they had to opt for medical treatments instead
of depression treatment. For example, a Latina woman said, “I stopped [the project]
because they told me that they were going to start on dialysis.” Similarly, another Latina
85
woman said, “Sometimes because of commitments, for example, I have another
appointment with a doctor. That is why I haven’t gone.” Patients seemed to be faced with
difficult decisions: a White woman stated, “Well I have no choice, because if I don’t go
for the medical appointments, I don’t get my medication.”
Depression Treatment Barriers
A clinically important barrier which was identified by dropout patients was
depression treatment dissatisfaction. Although most patients were satisfied with the
depression treatment they received, there were some patients (n=9) who complained
that the depression treatment intervention was not helping them, and they therefore did
not see a need to continue with treatment. Specifically they described dissatisfaction
with their assigned therapy—either the therapist or the form of therapy. For example,
one Latina woman stated:
They would put me into this discourse to ask me nothing but dumb things and
that’s why I didn’t like it. That’s why I didn’t go… because I thought that was the
treatment and because of that to go and hear that foolishness, it’s better for me
to stay at my home. I have a lot to do.
This perception of the therapy not being interesting was common among those who
were dissatisfied: “Well, there was nothing interesting for me. I did not see anything that
it was going to help me. I mean something if like a conversation that was really
interesting for me. But it was nothing like that.” This participant noted that she had other
concerns that were more pressing: “I was going to go through surgery to get my uterus
removed. I mean things like that. I was going through menopause. It was like a lot of
depression.” She stated that the questions she was being asked in depression treatment
were making things “worse” for her. Another common sentiment was that the treatment
was not effective, because it did not make the patient feel better: “For me, I didn’t feel
that made me better or anything. It didn’t make we well. Then, I left it. It’s the same, for
86
example, if a pill isn’t helping me, I throw it in the trash.” (Latina participant). Similarly,
another Latina participant said:
I started going but after a while I would see that…actually I wasn’t getting the
help I really wanted or needed. Basically…nothing was going on…I guess I
wasn’t feeling I was getting the help I needed at that moment. I was totally
depressed.
One Latina participant felt that she would rather address her depression by herself and
her friends. She did not feel that the therapist was invested in her:
It’s better to talk to one of my friends and I make my own proper decisions. And I
tried to get ahead of my depression. To give myself a desire for life that’s all, by
myself…I felt that [the therapist] I went [to] and attended to me--it was only
because, well, it was her job and she was killing time there.
Informational Barrier
Study misunderstandings presented as an important informational barrier which
seemed to contribute to patients’ decisions to dropout of treatment. Some patients
expressed a general misunderstanding about the role of a social worker, what
depression was, and their agreed participation in depression treatment. Although it was
the only information-related barrier, it is very important to clinical, as well as research
and informed consent implications, as will be discussed in the next chapter. Study
misunderstandings were the overriding informational barrier that seemed to contribute to
decisions to discontinue treatment. Some patients (n=6) expressed a general
misunderstanding about the purpose of the depression treatment, the role of the
therapist, and/or the fact that they were even in treatment. For example, a Black
Belizean woman stated, “I wasn’t getting no depression treatment. I don’t even know that
I was depressed and something like that...I don’t even know if I had a social worker,
honey.” When another woman (African American) was asked if she was aware that she
was in depression treatment, she said, “Maybe not totally. Maybe I thought you guys just
called me…I guess I really wasn’t aware [that I was in treatment]…”
87
Instrumental Barriers
Dropout patients discussed four concrete and practical issues which seemed to
contribute to the discontinuation of depression treatment: transportation (n=14), financial
(n=11), employment (n=11), and caregiving issues (n=3). Transportation was the most
commonly mentioned instrumental barrier and included problems related to personal
transportation, as well as public transportation that they often depended on. Proximity
was a related transportation concern, as many times patients lived so far from the
hospital that it was difficult to find transportation. Patients also described the extent to
which caregiving responsibilities of children and elderly family members contributed to
their non-adherence to depression treatment.
Transportation
As noted above, transportation was the main instrumental barrier that seemed to
affect participation in depression treatment (n=14). Many participants were not aware
that transportation was offered as part of the design of the treatment intervention. A
Latina participant said, “Like I told you, I didn’t have transportation and I didn’t know that
they gave it.” A Latino male said that it “would have been beneficial to receive the advice
of a professional,” but the site was too far for him and he did not drive. Some participants
had limited transportation and had to choose which member of the family needed
transportation more: “We had one vehicle between us and he had to use it for work…My
husband needed the vehicle and I said, ‘Just forget the study.’” (White woman).
Similarly, another participant said, “I had no vehicle of my own at the time. My husband,
you know, was working so I had no way of getting there.” A Latina participant related her
dropping out to an incident with her car brought on by depression:
88
I was very depressed and stressed and all that and I went into one of those
parking lots and I punched out my tires…That got to me worse because I was
stranded. My parents are older and they couldn’t come and help me.
This woman’s experience also illustrates the ways in which participants relate numerous
life circumstances to one issue, such as transportation in this case:
At the time if I had my own vehicle that my husband hadn’t stolen from me, I
would have [gone to treatment]… It wouldn’t have been a big deal, because I
drive myself to my appointments. And tomorrow, I’m going to have to go and get
my medication. They have me taking a hormone because I produce too much
estrogen. So they gave me a hormone. That’s the opposite I guess? And so I
have to go and get medication. It’s next to the woman’s clinic, in the trailer. So
I’m used to going. It’s not a big deal when I have my vehicle.
Some participants relied on others to bring them to the appointments, but seemed to feel
uncomfortable with this to the extent that they discontinued participation: “I used to
bother one person to take me and bring me back, but then later it couldn't be done.
Then, that's why I didn't want to return there.” In some cases the person who was
helping became unavailable: “My daughter would take me…At that time, she wasn’t
working and she would take me. But later, you kind of let it go. She had to go to work in
another state and I couldn’t go. It got very difficult.”
Some participants seemed to get frustrated with the question about why they
stopped treatment, when they attributed their discontinuation to a logistical issue such as
transportation: “Look I stopped because it was impossible at times to go all the way to
the General…That was my motive but in reality, I would have liked to continue it.” When
asked if she felt that it was a good decision not to continue treatment, the participant
said, “No I don't think that it was a good decision not to continue it. What happened, like
I said, it was because of the time and the distance and that was what it was.”
As can be seen, transportation barriers are often not solely a distance problem or
the lack of a vehicle, which causes a problem. Transportation barriers are often complex
89
multifaceted issues. Thus, providing transportation is not going to eliminate all
transportation problems.
Financial
Financial issues also permeated participants’ narratives (n=11) of why they did
not continue treatment. These narratives ranged from straightforward statements such
as, “It’s just a financial thing. I can’t afford right now to go to a psychiatrist.” (White
woman) to more complex explanations involving multiple overlapping issues, such as
concurrent medical conditions and social support issues:
I told her that if it was about having to pay something, I’m not going to be able to
[pay]. I don’t want to tell them lies that yes, yes, I will pay if I can’t because now I
don’t have the support of my husband. Now, I’m alone (Latina participant).
One African American woman said that she could not afford treatment and instead would
opt for smoking marijuana, which itself was also problematic for financial reasons: “I said
that when I get depressed I go smoke a joint…Not having any money, I cannot even go
smoke a joint; so I just stay bummed out.” Financial issues were often tied to insurance
issues, as expressed by this Latino participant: “My limitations on the insurance are too
high. We have Medi-Cal and it helps us only for emergencies.” Like the transportation
barrier, the parent study intervention design attempted to eliminate this barrier by
providing free depression counseling or medication treatment and not billing their Medi-
Cal insurance, but some patients still perceived financial barriers to indirectly be linked to
their decision to drop out of treatment.
Employment
Employment-related barriers were equally significant barriers for dropouts. For 11
participants, work responsibilities that they themselves were experiencing or work-
related problems that their spouse or family member were experiencing, interfered with
participation in treatment. For example, one Latina participant said that participation was
90
a “waste of time,” because she would have to “stop working and go over there.” Another
Latina woman said her health was a priority for her, but work was also a priority: “A big
priority for me is work because I am a woman, as you know, alone. I don't have to
depend on anyone.” Another component of the work barrier was when patients’ family
members worked and could not help with transporting them to appointments. As one
Latino participant noted, “My daughters…worked during the day and it was very hard for
them because…they had to miss a day of work to take me.”
Caregiving
A few participants (n=3) described considerable caregiving demands that
sometimes impeded their ability to and/or interest in continuing depression treatment. As
will be seen below, instrumental concerns such as caregiving often overlapped with
other concerns (e.g., caregiving and financial).
Patients had challenges in finding people to take care of their children while they
went to treatment, and they were unaware that childcare was available in the clinic (or
that it was available to their children). For example, one Latina woman said that she was
all alone. When asked if she knew about the available childcare, she at first said she did
not know about it, and then when this resource was confirmed by the interviewer, she
said, “They say that they only take care of the children that are born here.” Another
Latina participant said that she did not participate because she had no transportation
and no one to take care of her children. When asked if she mentioned this to her social
worker, she said she could not remember. Another reported adult dependent caregiving
responsibilities for a disabled spouse. For example, a Latino male said, “I have learned
to support and tolerate my woman. There is no other thing. She also suffers. She has
osteoporosis that bothers her articulation. She can’t walk very well. Then, I help her in
what I can. That’s how it is.”
91
Cultural Barriers
For this population of minority, predominately Latino immigrants, cultural barriers
often presented themselves within the dominant, non-minority U.S. culture. Dropout
patients described personal, and often emotional experiences of: 1) language-related
barriers and 2) perceived discriminatory treatment within the clinical setting. Language
and discriminatory barriers seemed to be related in many instances. Again, these
experiences may not have been directly linked to the patient decision to dropout, but
seem to contribute to negative feelings about the hospital providers and willingness to
receive treatment which was many times seen as “not really needed.” Prior negative
cultural experiences based on language, race/ ethnicity, or immigration status within
other departments of the medical center often also had a rippling effect into this study
decision to participate, as patients were often turned off and so discouraged about a
prior bad incident, that they were reluctant to seek treatment in other the ADAPt-C study
intervention.
Language barriers
Patients (n=7) described providers not speaking Spanish at all or speaking
Spanish very poorly, which created a language communication barrier (from both
depression intervention providers and other non-depression treatment personnel). One
Spanish-speaking Latino male described his frustration:
Yes also the lack of interpreters. They would call the interpreters, but it also
looked like they didn’t know how to translate the language. Yes that was very
obvious to me…many doctors there don’t speak Spanish very well. …Because
there aren’t many doctors who have the language capacity in Spanish to have a
conversation with you. Then, I can’t explain to him everything that I feel because
I don’t know English either. That was the barrier that there was. The first times,
my daughter who is a citizen here, accompanied me. But she isn’t over here now.
A Spanish-speaking Latina also explained a similar feeling of language causing a barrier
for her:
92
Well I speak very limited English. Then that is one of the barriers. Do you see? If
the entire world, if there was that type of barrier here, well there wouldn't be as
many problems. But that is one of the things that exists that well let's say that
impedes and that's a big problem ….
This same woman went on to explain that even when there was a Spanish interpreter,
the communication problem still existed:
For those that do not speak because that way they have a way of communicating
directly and not to have a translator that many times don't say things in the same
manner. The translator doesn't put it in the same language that you are speaking.
Despite this language barrier, this woman eventually received a Spanish-speaking
translator who was effective and could communicate with her. This woman felt more
confident about communicating with someone whom she can understand and she can
communicate with:
I try to understand or if not, they get like an assistant. I don't know what type of
work he does. And he helps me a little. For me at times, with the little bit that I
understand, I communicate with him and he is a person that is very straight, very
intelligent and he understands me. I think that I make myself understood because
I ask him.
Part of the communication barrier may have also involved an education or literacy issue,
as one Spanish-speaking Latina patient explained:
Well the truth is that I can’t read. In Mexico I didn’t study and here when I arrived,
well, I have the children and I brought them. I worked to bring them with me. And
I didn’t have time to study … Sometimes I think that … Because you don’t speak
English and then you don’t know how to read… I didn’t know how to speak
English, because the one that I got spoke almost all English. [She spoke] very
little Spanish ... It’s that at times you go around asking, “What is she saying?” or,
“What does she say?”
Discrimination
All dropout narratives which involved some element of racial/ ethnic
marginalization and vulnerability (n=5) depicted a barrier to wanting to access health
care organizational systems, including the depression intervention. These descriptions
depict the relationship between language and discrimination barriers. One Spanish-
93
speaking Latina woman directly acknowledged this relationship: “Yes, it was like
discrimination because you don’t know English.” This same woman went on to explain a
prior incident with a hospital secretary. This negative incident discouraged her from
continuing to participate in the study intervention:
I saw her (hospital secretary) like annoyed and then she said, ‘How long have
you been here in the United States?’ And I told her that I had more than 40
years. And she said, ‘And in 40 years you still haven’t learned English? ... get
yourself to study English. Study, learn it.’ she said. ‘It’s an embarrassment,’ she
said. ‘It’s a shame.’ she said that I have so many years and that I haven’t learned
English. And I said, “Well…”
Although another Spanish-speaking Latina woman did not personally experience
discrimination which prevents her from continuing treatment, she provides an
understanding of the extent to which discrimination can affect an undocumented
immigrant, resulting in the unwillingness to participate with a system in which there is
underlying marginalization:
I feel that those people [undocumented immigrants] suffer a lot. You know that
those people - well I thank God that I have all of that [U.S. citizenship
documentation], but when I didn't have it, I never felt like that never, never. But
those that actually don't have them, for me, it's a very large pain because there is
discrimination. I say that in this world there shouldn't be any discrimination. We
should all be the same and everyone is living in this world for something and not
to give one person preference and not the other one. That's why there is here so
much difference, so much racism, so much insecurity for those people because
they do have a very big barrier, the language, the papers more than anything.
And there are people that speak Spanish and English and they are - and they
discriminate a lot against those people, our race. And not all American
discriminate, but we as Latinos discriminate against other people. … You see a
complete culture with gringos - there are some gringos that are a little racist but
not all of them. They try to help you, but if that barrier didn't exist like you said, it
would be different here. But right now I've been watching the news and how
they're getting people without papers, separating the family. The poorer people
are the ones that suffer more and that's horrible. … It's very difficult because if
you come to this Country, it's to be better because, unfortunately, in our countries
there is too much corruption with the presidents and all those that roll down. And
here, well thanks to God, in this Country that doesn't exist that's why so many of
us come to this side … Yes and the people who don't like politics and want to
work are the people that suffer more because they don't have that position that
the entire world, that all their people have ...That is a big barrier, very large
limitations, and a very big discrimination.
94
Again another Spanish-speaking Latina woman also explained the general reluctance to
seek treatment when there is fear about repercussions of not being a documented
citizen:
And maybe, like you say, they don't have papers and they are afraid to see a
doctor because they think that there they will deport them. And these people will
tolerate the illness and don't get a doctors’ help. … And that's a very big
injustice. I feel it's something very inhuman.
Systems’ Barriers
Patients also described service delivery and patient-provider issues that interfered
with continuing treatment.
Service delivery
Patients (n=13) described similar instances of the long waiting for scheduled and
unscheduled appointments and pre-planning that has to be done to access a large
bureaucratic organization. Service delivery red-tape can be barriers for people accessing
a large public sector care system. The distinguishing element to this barrier was that it
was dissatisfaction involving the hospital organization or bureaucracy. These
experiences often made it difficult logistically to attend their depression treatment
appointments with the depression intervention due to timeliness of their medical
appointment or scheduling conflicts. Patients describe these negative experiences: “I
know that I can go sit all day at the county office - another thing; you go to the county
office you need to have money or you need to have lunch…. Yes and take something
really good to read; any bureaucracy”; “I would be there all day”; “You always have to
wait. One time they even forgot me there in the consultation room. They were turning off
the lights. It wasn’t until I started talking.”; “You get a new doctor every time you go
because they rotate wherever they have to go.”
95
Patient-provider issues
Patients (n=10) reported negative interactions from their clinical healthcare
providers, which interfered with continuing treatment. There also seemed to be an
association between patients who were dissatisfied with their treatment and patient-
provider issues. The distinguishing difference between the barriers which involved
treatment satisfaction and patient-provider issues was that depression treatment
dissatisfaction did not involve any dissatisfaction on the part of the provider, but more
general dissatisfaction toward the effectiveness of the treatment, itself. These
overlapping concepts are illustrated in the following narrative from one Spanish-speaking
Latina patient who said that the depression treatment therapist “would go out and I didn’t
know if I should wait for another visit… she would disappear:
I stopped going because I didn’t find anything in her that was even funny
because she didn’t ask any questions … yes that the only thing I did was sit
there, the two of us, but she didn’t talk to me … there was no attention … she
could have brought out the emotions that I had, but sometimes you find yourself
better outside with someone who is more useful. There she wasn’t useful to me
for anything. ..This same patient explains “the scolding from the lady [the
therapist] that I received, well it was better to leave … well, not scolding but just
the way they answer you … well, like she was always like in a bad mood. …No,
with the experience that I had, no. If they sent me back, I wouldn’t go. Not there.
Not there.
It is important to note that although this patient described being dissatisfied with
treatment, she did not feel entitled to complain. When the interviewer asked this woman
why she had not complained to someone, she said: “I'm very grateful to the hospital and
the people there and well, I don't feel justified in reporting those things like that because
they have done me more good that I should go around making complaints. A similar
rationale might be evident in the case where a Black Belizean woman described some
confusion about her medical care regimen, but not feeling comfortable about asking
questions: “When they give me medication I just drink it.”
96
A different Spanish-speaking Latina woman explained a similar personal
experience which speaks to both patient – provider issues which were negative, and
impacts decisions to remain in treatment:
Well it was because at that time that I had made my appointment, I was going
because I felt the necessity to talk to someone. ..Well then, it's very far from the
hospital and I would go. But when I went, for some reason, they didn't attend to
me. I wasn't appearing on the appointments and well, the person who was going
to see me that day, wasn't there. She went to do something else. And well, for
me, it was very far to go there to waste time. For what? That's why I didn't go
because when I wanted to talk to someone, something would happen and I
couldn't. So it was better and I didn't go … I felt that I wasn't important because if
a psychologist is going to talk with me it's because I feel psychologically bad. And
I'm going to look for her and she's not there, I'm not important. And for the most,
then I was more discouraged and decided not to go and to leave it that way.
Perhaps by myself I could get ahead.
Another Spanish-speaking Latina woman describes a negative experience with a
depression treatment therapist: “She gave me the appointment but the thing is she was
late for the appointment. So that’s why we didn’t have a chance to go really deep into
the conversation. I had to go for my checkup and we stopped everything.”
One Latino male brought up a challenging issue related to his interaction with his
provider who was not male. He felt uncomfortable talking about his prostate cancer:
“Yeah…you know an operation like this one butts you out of circulation….Circulation like
a man. Do you understand?....All of that affects you and many times are embarrassed to
talk with a person like a lady or with - do you understand?… it’s embarrassing for you to
tell a lady how…” This reluctance to speak to a woman about his condition might also
have cultural implications in addition to the clinical and gender implications.
Multiple Confounding Barriers
Most dropout patients (n=12) discussed more than one barrier which influenced
their continuation of treatment. Not only did they mention multiple barriers but they also
described barriers that were interpenetrating, or what will be referred to here as
“confounding.” The barriers were found to cluster according to several key domains.
97
Multiple confounding barriers capture the complexities of daily life and the chronic nature
of participants’ problems, many of which seem to have predated the study. These
multifaceted circumstances seemed to contribute to the unwieldly nature of the problems
they experienced, which when combined, influenced dropout. Figure 3 depicts these
clusters of barriers, shaded by domain, involved in patients’ dropout from treatment.
Table 3 represents specific examples of dropout narratives that reflect the finding of
multiple confounding barriers. These quotes illustrate the penetrating or “confounding”
nature of barriers that these patients often experience. Interestingly, family or support
issues were mentioned often in combination with other barriers; however they did not
individually present as a single barrier to treatment completion. The combination of
cancer or medical issues and, of course, the instrumental barriers, are clearly
mentioned.
98
Table 3: Multiple Confounding Barriers
4 Multiple Confounding Barrier Quotes
Cancer or
Medical
Depression-
Related
Information
Instrumental
Cultural
Systems
Family or
Support
Issues
1. “Or just to get something to make me not think so much about things like
my cancer or my son. Then I took care of my mother up until 2003 she lived
with us. And she passed away and you know. I dealt with… my father
passed away in ‘99 and she came to live with me because she lived up in
Modesto (California). I deal with a lot of things. Then after she passed away
in February, in March I got palsy. And I didn’t even know what it was. And
then the following July was when I started bleeding so bad. They gave me
like 8 bags of blood over a period of a week. Then I had the
hysterectomy….. So I’ve been through a lot over the years.…. But I don’t
think the counseling wouldn’t be the biggest thing in my life because I am
able to deal with certain things. I deal with the bills. I deal with my home. My
apartment. I deal with a lot of normal things that a lot of normal people deal
with. So I haven’t got where I’m not functioning. I think when I stop
functioning, that’s when I really have a problem.” (White American Woman
Dropout)
Cancer diagnosis, New medical condition,
Surgery
Financial issues, Caretaking of mother,
Housing
Relationship issues with son, Loss of father
and mother
98
Table 3: Continued
2. “Well, at that time family problems, very personal problems including major
health problems and economical problems because ultimately, my jobs
haven't been very stable. And the consequences of that, well, I went into a
state of depression and later I got more problems. Problems with vision,
physical problems and it has gone to family problems. Then I feel at times
that I have no way out. Then I fall into a very big depression and I get very
irritable … well, at times I get very tired of taking the medication. I'll leave it
let's say two or three days without taking it. And later I start feeling the
same bad feelings…. Many scary things that I feel.… I get very depressed
because of health. A little while ago, like I said, I went to Nicaragua
because my mother was very ill. And I had to go because of an emergency.
And with my problems like that, well no way. I had to leave and I was there
with her, like they say, spending and going to the doctor and many things,
but I have to do that.” (Spanish-speaking Latina Woman Dropout)
Health/physical problems, Vision
problems, Taking / Not meds
Depression, Irritability, Fear
Economic problems, Employment
instability, Caretaking of Mother
Family problems, Traveling to see ill
mother
3. “I don’t have a job. I’m sick. I don’t have nobody to take care of me, or
anything like that. There was a lot going on. I didn’t have a place to stay.”
(Black Woman Dropout from Belize)
Sick
Work
Problems,
Housing
Lack of
Support
4. “… yes, but in those times my son was on drugs and all of that. He was in
jail and all of that affected me also. Yes, I felt that I was depressed. And
then I also separated from my wife and my children. It was all the problems
that one has.” (Spanish-speaking Latino Male Dropout)
Depression
Problems
with son,
Family
separation
99
100
Additional concepts to explore
In addition to a better understanding of the aforementioned barriers to treatment, this
study aimed to also look at the influences of Attitude (from the TBP) constructs (which
include Behavioral beliefs and Outcome evaluations ) and Subjective Norms (which
include Normative beliefs and Motivation to comply). As formerly indicated, the Attitude
construct was examined to determine 1) Perceptions of depression and depression
treatment and the Subjective Norm construct was examined to determine 2) the Role of
social support. The purpose of this exploration was to further understand additional
factors which influence intention and, eventually, decision-making with regard to uptake
to theory.
Perceptions of Depression Treatment
More than half of the dropout patients (n=13) expressed an overall reluctance to
depression medication treatment due to their prior personal experiences with
antidepressant medication treatment. Patients were often reluctant about taking too
many pills, but were in favor of the benefits of counseling. Patients were typically vague
about their rationale for not wanting to take medication, as explained by a patient:
“Because it’s probably bad. But I don’t want to. I want to say that I’m going to get weak
on certain pills.” This same woman went on to describe her reliance on God, instead of
taking medication in dealing with her health. She described a conversation with a close
friend about not needing the medication, but instead relying on her spirituality:
Listen what pills are you taking? ‘What?’ Yes, what pills are you taking? ‘Pills?’
Pills for what or why? ‘Yes,’ she says, ‘For the depression for all of that.’ Oh, I
said, I’m taking two kinds of pills. ‘Which ones are you taking?’ I told her, I’m
taking body and the blood of Christ.
Another Spanish-speaking female Latina patient also described how she relies
on God, instead of taking medication:
101
God is a better. Well, I have friends that are with psychologists, but I have never
liked taking pills. Those people tell me that they take pills to sleep and pills to
have during the day and for night. And I don't like that - the pills even if they're
prescription because I think that you have to control over the problem; first
through God; and secondly, you have to have the desire to deal with these
problems. And I think that I don't need pills at this time.
Others explained their reluctance due to the belief that antidepressants could be
addictive. A Spanish-speaking Latino woman explained this rationale for such beliefs:
I didn’t want any medications because I told them that they should give me the
options so that I could know partly on advice, of what I could do and if this could
go away because I didn’t want to become addicted to pills.
A Spanish-speaking male Latino patient also described reluctance due to fear of an
addictive quality of the medication:
I think that the treatment, well, was going to be pills. I’m taking enough pills for
the question of diabetes…. There are times that the medications get into your
body and then later you can’t live without them. And that’s the hardest thing for
you. I would say that it would be better to see a counselor …
Another Spanish-speaking Latina woman described this additive quality:
I imagine that the pills like they will govern you, like a control and that if I take
them, I won't feel good. And if I don't take them, will I'm going to go around like
that. … You have to be taking them always and always. I don't feel that it's good.
You have to have the control on this problem unless it is something that is very
serious and that you needed assistance. But meantime, I imagine that just with
talks or that you, yourself...
A Spanish-Speaking Latina woman described a reluctance based on already taking
other medication for other medical conditions and previous side effects:
Yes every day. I’m taking two pills… she was giving me another pill, but what
happened is that she said she was going to take that pill away because it didn’t
go with diabetes because I got diabetes. And then she took that one away and
she gave me another one but my head hurt too much. And then, later, she gave
me another one but what did that pill do? I don’t remember what it did to me. … it
was unpleasant to me and I no longer took it.
However, this same woman also goes on to admit a general stigma about
antidepressant medications: “Many times they say that medication is for crazy people."
102
Another Spanish-speaking Latina woman describes her negative experience with
antidepressants which contributed to her current unwillingness to take these
medications:
I thought that it would be something that, in reality, would help you. But no, the
pills I took every once in a while, but I don’t like to be drugged up all the time.
And all that medication, that’s what it’s for to make you feel down; to make you
feel relaxed and all of that.
Dropouts (n=17) overwhelmingly were in favor of counseling for depression
treatment. One Spanish-speaking Latina women admitted to agreeing to counseling
treatment out of “curiosity”:
I did it out of curiosity to see what that was. To see how and to see, to have
experience in something that I hadn’t had before; to see what kind of thing it was;
to see if it would help me; to see that. Well, naturally I was depressed like you
said, but I went for that to see if it would help me.
Most patients were initially convinced about the benefits of therapy, over
antidepressants. Even after their short counseling experience with the PST counseling,
most favored talking about their concerns, over medication:
I felt good talking to them because I could get everything out and I could, well,
feel good. And I did feel good when I talked to them. When it was personal, also
and when it was over the telephone, like I said, it was the last two calls that I got
from them over the telephone, the therapist.
I enjoyed talking to her because sometimes you don’t feel very comfortable
talking to people that know more than you do and that counsel you better and
more when it’s because of illness or because they can give you a reason so that
you can feel much better. And that is what motivates you so that you can be
more content.
Role of Social Support
Dropouts were asked about the role of supports in their lives which potentially play
an influential role in their health decisions. Patients described the following types of
103
support: a) informal support, which often included Instrumental support, b) reliance on
self, and c) religious support. All patients reported having informal networks, although
some were not able to access them due to living in another country, practical barriers, or
strained relationships. Even when support was available, patients still seemed to feel a
sense of reliance on themselves. Patients, and particularly those who did not feel that
they should trust other people, often relied upon spiritual support.
Informal support
All dropout patients’ (n=20) informal support networks of family and friends had
significant influences on patients’ lives. Patients reported adult children, spouse,
grandchildren, parents, and involved friends who were easily identifiable and they could
readily identify whom they could call upon, if needed for various types of support.
Patients described the usefulness of family and friends for motivation to uptake a
particular health behavior or advice in making decisions, especially during their illness
and other life concerns. For example, this Spanish-speaking female Latina patient
reported the advice from her mother:
And well at times, sometimes I ask her everything and I tell her everything,
everything, that I'm going to do. And then, she always tell me, "Look you have to
go to your appointments or you have to take that medicine." Or when they were
going to give me chemo, I didn't want it, but she would motivate me that I should
want to...She’ll telling me in this case that I should take the chemo. That I should
take the chemo, and ask God for help. And like that, talks like that that I
sometimes make many decisions because of her advice.
A Black Belizean woman also described the advice that her distant relatives (still in
Belize) have:
Well, I’m their sister, and that’s my mother, and if you see a child sick you will say
- tell - give them advice to go the doctor, and to see a doctor for them to get
better, and the same as my sister.
One female Latina patient also described the protective nature that families have
over them: “They are my daughters. … they are watchful about my taking my medication
104
that they go to the doctor. They make sure I don’t miss dialysis. And well they are
watchful over me. “
However, many of these patients’ families live outside of the U.S. and they are
unable to gain emotional and practical support, despite their need during their illness.
One Latina woman described this common situation: “My family has been helping and
supporting me. I don’t have very much family here, but they have supported me--my
sister that I have in El Salvador. “
Even patients whose families are local described some inability to provide all
needed support due to the informal support network having their own daily
responsibilities (e.g., work, caregiving). This woman described her situation where her
husband died and her adult children are busy:
Now that my husband is gone, for me it’s been very hard. That was on May 30th.
For me it has been very hard. I have my daughters, yes, and they are very good
daughters and everything, but they are married. They’re in their homes.
A Spanish-speaking Latina woman described her solid support network,;
however, her daughter‘s life demands do not allow her to assist her mother as much as
needed:
Like here with my daughter. Well, with the one that I live with her. She works.
From her work to the house and she arrives and she does something with her
children and that all those things. It’s logical, well it’s their lives. You are now past
being a parent. You are not past being second and you are third. … to have the
support, the moral support, no and economically it’s less.
Patients also discussed the reciprocal role of support in caregiving that provides
satisfaction and worthiness to the patient. This becomes somewhat of a challenge, often
given the patients’ inability to perform normal caregiving activities. This Spanish-
speaking Latina woman noted, “I have to do things well for [my children]. “
Patients also described instances where family is called upon to provide
instrumental support, however barriers get in the way (e.g., pre-existing marital
105
problems, living out of the area or country, or financial barriers). Some described non-
supportive spouses, for example. A male Spanish-speaking Latino patient described his
wife’s inability to cope with his illness:
The treatment that I had for cancer, she didn’t take [it] very well because like they
said, my wife is a little authoritative and she didn’t treat me well. And she thought
that I was making up the illness so that I didn’t have to do anything.
Several patients described an inability to really confide in and talk to others who are
close to them. For example, a Black woman dropout stated, “I really find that my life is
easier when I don’t get involved in other people’s lives.” This English-speaking Black
woman said:
I really don’t have any friends. I am kind of a loner. My daughter and I talk a lot –
my daughter that I am 16 years older than. She is my one true friend and that is
kind of sad. I am kind of guarded when it comes to people on the outside; I guess
I trust and I don’t trust them because I have been burned.
A Spanish-speaking Latina patient said that she has a few friends but she does not “tell
them [her] personal business.“ Another Spanish-speaking Latina patient said that she
wished she did have someone to “talk to in confidence,” but that she did not talk to
anyone because she did not feel she would get the desired response: “Sometimes you
go to tell your friend the problem and what they do is divulge it or they don't give it any
importance.”
Patients who did not have adequate support often described difficulty in trying to
face the different issues that present within the cancer continuum and daily life
problems:
I’ll tell you that I have no one. I talk to my daughter at times about something,
with my sister and with my friends little things. And from there I get conclusions:
one opinion, from another opinion and another opinion and I put them together
and then I make of summary of which one is the best.
Reliance on self
106
A second domain of support came from within the patients themselves: 75% of
dropouts described relying on themselves to some extent for coping with their illnesses.
Some patients expressed that their illnesses or “problems” were their own to handle. As
one Spanish-speaking Latina patient stated, “My problems are mine…I have always
made my own decisions.” This self-reliance was related to “functioning,” as expressed by
this English-speaking White female patient:
I don’t think the counseling wouldn’t be the biggest thing in my life because I am
able to deal with certain things. I deal with the bills. I deal with my home, my
apartment. I deal with a lot of normal things that a lot of normal people deal with.
So I haven’t got where I’m not functioning. I think when I stop functioning, that’s
when I really have a problem.
Religious support
When informal support was not available (and in some cases even when it was),
patients (n=11; 55%) relied upon their spiritual beliefs to help them through their
illnesses. One Spanish-speaking Latina patient said that she did not have any friends
because she did not trust people, but she did have a “Savior” who gives her “a lot of
comfort.” Similarly, an English-speaking White woman said that she feels strong
because of her “faith in God.” When asked how God helps her with her depression, a
Spanish-speaking Latina patient said, “My faith and religion helps me a lot. It gives me
strength. I believe in God and all of the saints. God has helped me and blessed me by
letting my out of my cancer.” One Spanish-speaking Latina woman said that she did not
like taking pills, and that instead one has to “control the problem, first through God, and
“secondly, you have to have the desire to deal with these problems.” Several
respondents said that they prayed to help cope with their illness
107
Aim 2: Completers’ perspectives, barriers, and enablers to depression treatment
adherence
Thematic frequencies were used to explore the differences between completers
and dropouts in order to 1) identify the barriers across the two groups, and 2) identify
depression treatment enablers that promoted retention. As a juxtaposition to describing
the comparison of barrier frequencies across the two groups, the most divergent
frequency group characteristics will be examined, with particular emphasis on clinically
important areas. Completers’ narratives of enablers will also be briefly explored as they
relate to findings from the dropout group. As mentioned in Chapter 3, the completer and
dropout interview probes were similarly organized and sensitized from the theory,
particularly from the TPB framework. However, completers were asked about “the things
that often made it difficult for them to attend treatment.”
Barrier Comparison
Table 4 contains the comparison of barriers by study participation group. It is
important to first analyze barrier frequencies within each cell to highlight the
discrepancies between the completer barriers and the dropout barriers. There were
equally (60%) as many completers as dropouts who described general multiple
confounding barriers related to the complexities of daily life and chronic nature of their
problems, yet the completers could complete treatment in the face of these barriers and
the dropouts could not. Potential explanations for this difference can be found within
some of the discrete barriers themselves. All dropouts (100%) and most completers
(60%) thought the emotional impact of their cancer diagnosis interfered with depression
treatment continuation; however, the interference of cancer treatment caused more of a
problem for dropouts (55%) as compared to completers (10%). With regard to
depression treatment barriers, more dropouts described dissatisfaction with depression
treatment (45%), as compared to completers (0%). However, in both completer and
108
dropout groups, counseling was preferred (80% of completers and 85% of dropouts).
Dropouts (65%) expressed more medication reluctance to depression treatment, as
compared to completers (10%). More dropouts (30%) expressed misunderstandings
about the study, whereas no completers described such misunderstandings (0%).
Although completers described experiencing just as many multiple confounding
barriers as did dropouts, far fewer completers described experiencing specific
instrumental barriers. The majority of instrumental dropout barriers were due to
transportation barriers (70%), whereas no completers described transportation barriers
(0%). In addition, there were more instances of dropout patients describing (either direct
or indirect) financial issues contributing to treatment completion (55% of dropouts and
30% of completers). Also different from completers, dropouts complained about multiple
types of socio-cultural barriers (i.e., “not having your papers”, language [35%], or
discrimination [25%] issues) that they felt made it difficult to continue treatment, whereas
completers described no instances of these barriers interfering with treatment. With
regard to systems’ barriers, dropouts described many more (65%) logistical service
delivery barriers, as compared to completers (10%); and many more (50%) patient-
provider issues, as compared to completers (20%).
Table 4. Comparison of Barriers to Depression Treatment
Frequencies: Completers
(N=10)
Dropouts
(N=20)
Experiencing Multiple
Confounding Barriers
Multiple Confounding
Barriers
6 (60%) 12 (60%)
Experiencing Cancer-
related barriers
Emotional impact of cancer
diagnosis
6 (60%) 20 (100%)
Cancer Treatment 1 (10%) 11 (55%)
109
Table 4, Continued
Experiencing Depression
Treatment Barriers
Depression Treatment
Dissatisfaction
0 (0%) 9 (45%)
Medication Reluctance 1 (10%) 13 (65%)
(Counseling Preference) 8 (80%) 17 (85%)
Experiencing
Informational Barriers
Study Misunderstanding 0 (0%) 6 (30%)
Experiencing Instrumental
Barriers
Transportation 0 (0%) 14 (70%)
Financial 3 (30%) 11 (55%)
Employment
(which included
spouse/patient work)
3 (30%) 11 (55%)
Caregiving 0 (0%) 3 (15%)
Experiencing
Sociocultural Barriers
Language 0 (0%) 7 (35%)
Discrimination 0 (0%) 5 (25%)
Experiencing Systems
Barriers
Service Delivery 1 (10%) 13 (65%)
Patient-Provider Issues 2 (20%) 10 (50%)
Completer- Dropout Enabler Supports Comparison
Table 5 shows the frequencies of enablers to depression treatment retention and
sustainability. Both groups were asked about supports that they have in their lives to
encourage and enable them to continue with treatment. Despite the finding that dropouts
had more informal support (e.g. friends and family) than completers (100% versus 80%),
dropouts had a greater inability to use these informal support networks (50% for
dropouts and 20% for completers). Fewer completers (10%), versus dropouts (55%)
thought religious support influenced them to remain in treatment. An unanticipated
concept that emerged from the interviews was self-reliance in coping and continuing with
treatment. Completers described this “self-reliance” as the need to rely on themselves
110
because they did not have an outside formal or informal source of support to meet their
emotional or practical needs. Both completers and dropouts expressed a common need
to “do for self.” Both groups explained that they still felt that they could not bother others
or trust others to always assist with many emotional needs. Although they both
described a “reliance on self” in coping, questions probed for completers thoughts on
particular clinically desired enabler strategies that retained them to treatment.
Table 5: Comparison of Enablers to Depression Treatment
Important Enabler
Supports
Completers
(N=10)
Dropouts
(N=20)
Informal support exists 8 (80%) 20 (100%)
Reliance on Self 7 (70%) 15 (75%)
Religious Support 1 (10%) 11 (55%)
(Inability to use informal
support)
2 (20%) 10 (50%)
Clinically Desired
Enablers
Clinical Engagement
Important
9 (90%)
Discussing Cancer 7 (70%)
Getting “Advice” 7 (70%)
Therapist Flexibility 3 (30%)
Clinically Desired Enablers
Completers described specific enabler strategies which helped retain them to
treatment; these are referred to here as “completer enablers.” Shaded frequencies
highlight clinically important discrepancies. Since the focus here was to obtain the
enablers which kept patients in treatment, it made intuitive sense to focus of the
facilitator strategies of completers, not on dropouts. For this reason, data on clinical
enablers of completers were obtained. Completer enablers to depression treatment
included: 1) the importance of clinical engagement in the therapeutic relationship (90%);
2) the therapist “discussing cancer” (70%); 3) getting “advice” from the therapist (70%);
and 4) therapist providing flexibility with appointments (30%).
111
One Spanish-speaking Latina completer best illustrated this range of important
enablers and strategies which were important to her completing treatment:
They motivated me with - like everything my psychiatrist helped me because I
was not normal. I still am not normal but I think that I’m better. But for me, that
helped me. I was in the waiting room and they asked me if I was interested in
depression care. I just started crying. I didn’t even know that I was depressed. I
didn’t talk to anyone about my feelings. They reached out to me. I didn’t know
how to express myself and they taught me that. No one knew that I was sad and
now I could tell them because of what they taught me.
Therapist ability to clinically engage patient as a completion strategy
Completers expressed that one of the most useful strategies for retaining them in
treatment was providers’ engagement, i.e., ability to go beyond the treatment sessions to
ensure and establish rapport and a connection with the patient. Completer patients
discussed how clinical engagement was a key factor in them continuing and completing
treatment. As one Latina woman completer described, “Oh it was very good because
they gave me a lot of attention. They would always call me for the appointments and
they were always worried if I was going to the appointment. They were very friendly with
me and they gave me - for me it was good service. I like it. A White woman completer
described the mechanism or active listening which helped to establish the clinical
engagement and rapport:
I think people need to have someone that they can talk to. They need to feel
safe. They need to feel that somebody cares. Sometimes there’s things that they
need to say that they can’t say around their friends without the fear of any
repercussions. They need to be heard, completely gotten. Personally I believe
that when we’re listened too is one of the most healing elements that someone
can give us is to just listen to us and hear us.
Another Latina woman completer described that her “therapist used to go and
visit me no matter where in the hospital I was. I appreciated this.” Completer Patients
seemed to appreciate the extra attention and genuine interest they felt from their
provider or therapist.
112
Discussing cancer as a strategy to completing treatment
Completer patients also discussed how their therapists remained open to
discussing cancer-related psychosocial concerns; this openness about cancer
contributed to their retention to depression treatment. For example, one White woman
completer said, “It was sort of a lifesaver for me because I felt initially that I was judged
by a great many people in my community. The counselor that I got was a good listener,
and she was able to really get my whole world and then able to interject what’s really so
about my cancer - about me and my cancer.” Completers also discussed the knowledge
that remaining open to discussion about their cancer could provide. A Spanish-speaking
Latina completer, said, “I felt very good when I talked with her of my problems, of the
cancer and all of that. I felt good talking to her. And another Latina described the
importance in addressing the issue of their cancer diagnosis in therapy: “Yes it helped
me a lot…Well, the help that they give me was for the cancer also. To be able to, how
should I say, to be able to overcome that trauma. Do you understand?”
Getting “advice” as a strategy to completing treatment
Completer patients also described how getting advice was a factor in their
continued participation and completion of treatment. For example, a Latina completer
said:
She showed me what I needed to do. She was giving me advice … Like I didn’t
have to return to the way that I was, right? She helped me with the hours that she
gave me on that sort of thing, I felt like at peace. But at times that sometimes
doesn’t come to mind and then if I go back to what she taught me …She even
prepared me with flyers and handouts and I reviewed everything…
Another Spanish-speaking Latina completer explained how her therapist’s
“advice” helped her and described her appreciation of “advice,” by saying, “Well I felt
very comfortable with the therapist. Yes that and I liked what she told me. And while I
113
was there, it benefited me. She gave me help, advice, I learned different things. I didn’t
have to take so much medication.”
Appointment scheduling flexibility as a strategy to treatment completion
Completer patients described the “ease” with which they were able to see their
therapist and finish their treatment. For example, a Spanish-speaking Latina said, “it [the
therapy] was not difficult.” And a White woman patient described this as, “It [the therapy]
was easy. She would come and see me in the waiting room.” A Spanish-speaking male
also described this convenience and “ease” in seeing his provider, by saying, “[It was]
easy because it was right there when I went for my appointments with the doctor, there
they told me right here with us and they would introduce themselves … I would fill out
papers there … I saw for myself because I liked it.” Another Latina woman described the
scheduling convenience which made it easy for her to attend: “The fact [that] the
appointments were already made and that I did not have to make them helped me stay
in treatment.”
Aim 3: Provider clinical notes offer additional barriers to treatment completion
Clinical provider notes offer clues to a model of patient barriers which contributed
to dropout, from the clinicians’ perspective. Andersen & Newman’s (2005) recent model
of the social and individual determinants of medical care utilization provide the best
representation, detailing the various provider-indicated patient depression treatment
barriers. Clinician notes were analyzed from fifteen dropout patients who gave
permission for review of clinical notes. As compared to Phase 1 patient reported
barriers, providers also reported similar patient reported Instrumental barriers (e.g.
caregiving, transportation, financial, and employment-related), Cancer-related barriers
(due to cancer treatment and cancer symptoms), and System’s barriers (e.g. patient-
provider issues and service).
114
Figure 4 illustrates the provider explanatory model of dropout patient barriers
based on the Andersen & Newman’s model (2005) components. Providers reported
Predisposing component barriers related to patients’ Social Structure (e.g. seeking work,
work time conflicts, caregiving, “housing instability,” patient “moved” which led to
proximity problems, lack of social support, job stress, and transportation problems).
Providers also reported 2 levels of Enabling component barriers related to the original
Family and Community model levels. Provider documented Family barriers included
family “financial problems”, “conflict with the family”, poor family communication”, and
family “attitudes regarding cancer.” Community level barriers included the change of
medical and mental health providers and patient dissatisfaction with hospital staff and
medical services. Perceived Illness level barriers included patient health concerns,
chemotherapy treatment barriers, and the severity of the illness-related “pain.”
Evaluated Illness Level barriers included their existing cancer diagnosis and other
existing co-morbid illness (e.g. diabetes and hypertension).
Providers documented neither patient reported Depression treatment barriers
(e.g. treatment dissatisfaction and reluctance to medication) nor Cultural barriers (e.g.
language, gender, immigration, and discrimination). The limits of this data precluded
barriers at the Predisposing Demographic component level due to this being a qualitative
study design and not a quantitative one. The quantitative design of this phase precluded
collection of patient demographic data and an assessment of Predisposing demographic
barriers. In addition, information on Predisposing Belief variables was unavailable due
to the absence of data on patients’ expressed beliefs in the provider clinical notes.
This study phase also identified two emerging barrier components documented
by dropout providers: Enabling barriers and Additional psychological coping barriers.
Enabling barriers related to the patient’s family (e.g. conflict with the family and poor
115
family communication) and additional psychological coping issues (e.g. patient
reportedly “feeling better”, patient “not interested”, “emotional stress”, grief over a “loss”,
“panic attacks”, and anxiety over desired pregnancy”).
116
Figure 4. Provider Explanatory Model of Dropout Patient Barriers
* Shaded areas represent provider-indicated barriers that were not reported from patient
interviews
PREDISPOSING ENABLING ILLNESS LEVEL
Demographic
Social Structure
Beliefs
Seeking work
Work time conflicts
Caregiving demands
“Housing instability”
Moved/proximity problems
Social support lacking
Job stress
Transportation problems
Family financial
Problems
“Conflict with family”
Poor family
communication
Family “cancer
attitudes”
Health
Concerns
“Chemo
Treatment”
“Pain”
Severity
Changed providers
Dissatisfaction with
hospital staff &
medical services
“Feeling better”
“Not interested”
“Emotional stress”
Grief over a “loss”
Panic attacks
Anxiety over desired
pregnancy
Family Community
Additional
Psychological Coping
Perceived Evaluated
117
Aim 4: Provider strategies and perspectives on decreasing patient dropout and
increasing retention
Interviews with study providers allowed for a better understanding of potentially
viable retention strategies for low-income, minority cancer patients in a depression
treatment trial. Providers acknowledged that patients experience a whole host of
confounding barriers which interfere with completion of treatment. Many of the same
barriers that patients discussed were also recognized and echoed by providers. For
example, one LCSW summed up these barriers and said, “What I remember hearing a
lot of times was transportation, mostly transportation, feeling ill, and a lot of times just the
many crises that go on. Somebody got sick or they had to take somebody to the
hospital. Maybe they are the only source of support for somebody else. They had to be
a caregiver to another person. Or they themselves just got sick. Sometimes they had to
maybe go away. They were caring for somebody far away. They couldn't get to us. So
transportation then again was a problem because nobody could bring them. Or
somebody could bring even if they were far away it was doable but otherwise they
couldn't get to us.”
Providers suggested several strategies, which consistently addressed the different
types of barriers described during patient interviews. Table 6 provides detailed examples
of specific strategies that providers thought would be useful in addressing patient
retention issues. These strategies are discussed here with reference to the types of
barrier mentioned in patient interviews. This table provides a detailed list of all identified
strategies; however, the discussion will only elaborate on those strategies which seem to
be predominant and striking and does not go into an explanation for each individual
strategy. Although providers did acknowledge cancer and illness-related barriers, they
did not suggest any cancer-related strategies which would be appropriate or feasible for
118
depression treatment providers to address. Suggested depression treatment strategies
included patient satisfaction surveys, use of provider “self” to strengthen the therapeutic
alliance, and clinical motivation strategies. According to providers, informational barriers
are best addressed by use of further education about the study and psycho-educational
strategies. Instrumental strategies are best addressed by patient navigator and case
manager presence in assisting with concrete recruitment and retention efforts. According
to providers, cultural barriers are best addressed with patient-provider cultural and
language congruency. System barriers were seen from a slightly different perspective
and were expressed as being a provider barrier to effectively recruiting hard-to-reach
patients. Provider suggested strategies for addressing such barriers involved building
rapport with auxiliary hospital and clinic staff and reestablishing routine communication
between the study provider and medical staff.
119
Table 6: Provider Strategies
Patient
Barriers
Corresponding
Provider Strategies
Specific Examples of Additional
Provider Strategies
Depression
Treatment
Patient Satisfaction I think that you know what I thought would
probably help maybe a little bit would maybe
have a little survey, a little like confidential kind
of like survey. And maybe somebody looking at
it and if maybe that patient is not happy with the
social worker that’s working with them, maybe if
they can rotate to somebody else. Maybe that
would help kind of because sometimes you
know we might not you know be receptive really
well with one person. But maybe with
somebody else, we might…. and for them to
write you know I’m not happy with this person
because this and this and this. And they could
put would you like to you know work with
another social or somebody else. And then,
maybe they’d be more willing then to stay on
because sometimes when you’re not happy,
you’re not to keep going to the same person
you’re not happy with or you’re not comfortable
with. (Patient Navigator)
Therapeutic Alliance
Strategies:
• Building
Rapport
• Early
Engagement
• Active
Listening
Therapeutic Alliance - I believe that the
therapeutic alliance is very important in patient
retention. At its core, the alliance should be
focused on the needs of the patient. If the
patient believes that their needs are being
addressed in a timely and sincere manner, the
greater the likelihood the patient will continue
treatment; this of course speaks directly to
patient satisfaction from the treatment.
(Psychiatrist)
Building Rapport - Right because then if they
already see that you're not committed, and
you're not connected and you're not feeling - if
they automatically see that there is no good
rapport and there's no good connection there,
they are already going to come in with the
pretense notion like you know what; eventually
they are going to end up not coming in. I mean
I can't say that for all patients, but it just - I
think, yeah it's just really, really important for
them to feel comfortable with you. Because
they are going to be coming in to express - to
come in and talk about their personal therapy
they didn't even talk to anyone about. (MSW)
Table 6: Continued
120
Early Engagement - I think if they are not
brought on board immediately right then and
there I think the relationship or the end of
therapy doesn't go as smoothly as the therapist
who really spend their time, engage, and start
developing a relationship immediately. (Project
Manager)
Active Listening - Like for example, this past
weekend, I called somebody and she said, ‘I’m
feeling very depressed.’ And you know she
said, ‘Because of what I told you last time.’ And
I said, ‘Could you remind me what you told me
last time.’ And she said, ‘Because my son he’s
got a life sentence.’ And so then we just kind of
just chit chatted about how she was feeling
about that. And then like the whole rest of the
interview was smooth sailing… And I mean I
don’t know if she has somebody to talk to you
know. And so what if it takes five minutes of my
time to listen to her talk about how sad she is
that her son got a life sentence. (Project
Assistant)
Clinical Motivation
Strategies:
• Persistence
• Consistency
• Outcome-
focused
• Validation
• Family
involvement
• Strength
identification
Motivational Strategies - I believe that
motivation is the key to follow through with
treatment of any kind. Satisfaction and belief
that treatment is effective are both strong
motivating factors for treatment compliance;
some others include the strength of the patient-
clinician relationship (e.g. patient will follow
through because of not wanting to disappoint
therapist) and family support for the treatment.
(Psychiatrist)
Persistence: Being persistent you know with
them. And you know calling them when they
miss their appointment and letting them know
that it was an appointment for their health so
just being persistent … Definitely, definitely and
even like I think I had joked about this, but I
would call them and they would tell me. They
would pretend they were someone else and
they would say that they weren’t home. But
then I would end up like okay and I knew it was
them, but I would keep calling you know. And
so eventually they’re like, ‘Oh okay. Now we
have to say hello to her.’ (MSW)
Table 6: Continued
121
Consistency - And I think like I said the
consistency and really reinforcing the
importance of consistency in the treatment
process and not allowing them to drop out. If
they missed a week thing, ‘Hey I understand
you have to miss a week. But we need to get
back on track.’ To just making sure that as a
therapist, I was keeping the tone for how this
process was supposed to be. (MSW)
Outcome-focused Counseling - So I had two
patients who were very difficult to get them to
complete the PST. And I had sent them letters.
I called constantly to get them to come in. We
would do telephone sessions. But what I think
got them to stick was my ability to keep bringing
them back, okay remembering the stressors
that they had. Remembering those little things
that they would tell me that they would be
worried about or remembering what their goals
were. And they appreciate us because by the
end when they finished, it was if we were you
know I was congratulating them on their
process and reflecting back on all of those…
Accomplishments and also those barriers that
they had overcome. And I could see that they
finally felt a sense of accomplishment. (MSW)
Validation of Problems: I think by alliance, if I
had to pick one, is go with them and yeah this
system is frustrating. Because of lot of them
come in and talk about that they can't see
doctors. So I kind of like acknowledge that and
I'm kind of like, yeah I hear your frustration.
You know it's kind of like; I try to kind to like
again now you're asked to come and to spend
extra time here. I know that's really hard but I
thank you for coming. (MSW)
Family Involvement - Buy in and support of
the treatment by significant family members will
also contribute to retention of patients to
treatment. (Psychiatrist)
Strength Identification - I kind of, what I do is I
kind of focus on their strengths. I kind of say,
okay when was - do you remember when the
last time that you had such a hard time getting
through this problem and you did. You were
Table 6: Continued
122
able to resolve it. You were able to get through
it. And you usually they are going to say, oh
well this was years back. And I'll say look at it.
You see how you could do it. You see all the
strength that you have. You see how resilient
you were. So just asking them questions about
the times in the past when they were able to get
through tough times. And just focusing on that
and how they were able to get through that.
And how all the problems have solutions. You
just got to look for them. (MSW)
Informational
Educational
Strategies:
• Education
about study
• Psycho-
education
Education - Even if they are a little ambivalent
if you don't show - if you don't provide some
kind of or establish the trust at the very least or
show some kind of hope or begin to maybe help
them see some results quickly, you're going to
loose them. And so that requires the education
for me. For me the education was so key
because without that they were not going to buy
into it. There's just no way that you can engage
them as far as I'm concerned. (LCSW)
Psycho-education - It is very important to
obtain a complete psychiatric history of the
patient before recommending any form of
treatment. This will aide in determining the type
of treatment for the patient, based on patient
preference. Educating the patient about the
pros/cons/side effects/ alternative treatments to
antidepressant medications will greatly aide in
improving compliance and follow through with
any treatment. It also will build confidence in the
patient regarding the care and thoughtfulness
the clinician will bring to the alliance.
(Psychiatrist)
Instrumental Concrete Patient
Navigation and Case
Management Strategies:
• Transportation
resources
• Maintaining
contact
• Reminder calls
• Phone
communication
• Flexibility
Importance of Patient Navigation - All of the
aforementioned barriers make it difficult for
patients to continue treatment; however, the
social workers and case managers are very
helpful in this regard. I strongly believe that
each patient should have a case worker or
social worker (or a team of case workers)
assigned to his case; such a person can assist
the patient to navigate the system if needed.
Such a program is akin to students having
access to a counselor or academic adviser if
need arises. I believe that such an approach to
providing patient care need not be cost
prohibitive. (Psychiatrist)
Table 6: Continued
123
Arranging transportation I think a van would
work better and I think that would make them
come. Not make them come, but that would
motivate them to come when they know
someone’s going to be there to pick them up at
a certain time. You know and then someone be
there to take them back home. It would feel
more comfortable. (Patient Navigator)
Arranging transportation - I think like a couple
of patients they prefer taxi's because they are
on a wheelchair. Others they will just take the
bus, so we just give them a token. So I think
there's a more of a preference with the taxi
because it's more personal. But I mean I've
had patients who aren't - didn't mind the token.
But then we reimburse for gas mileage and that
was a plus for patients. (MSW)
Maintaining contact person - Yeah, I'm
thinking like if at the beginning when the person
was recruited - okay, because one of the other
now that I'm thinking about this, one of the
other barriers is they are so moving, you know
moving from place to place. But even people
who move place from place or apartment to
apartment have somebody that whether it's
grandma or whatever, they live in the same
house forever and you can always go there.
Maybe if we had asked for a like who is that
person for you. And what is their name? What
is their number? And we could call them up
and even if they said well, they are in Mexico
for the next six months. That would have made
it easier on us in the intervention to say okay
well, we're not contacting that person for six
months. Versus trying to hunt, people down
constantly … I mean it's not even somebody
like a partner who has to be like with them all
the time. It's somebody like grandma who
knows, well I haven't seen them in six months,
but I know they are in wherever. That type of
person. (Project Manager)
Reminder calls - Sometimes they would you
know cancel on me or you know things like that.
And always calling ahead of time to verify
whether or not they were going to make it, that
was always key, reminding them cause a lot of
Table 6: Continued
124
them you know they end up having all sorts of
memory issues because of depression and
what have you. And they wouldn’t remember
their appointments. I would always call them. If I
couldn’t reach them the night before, I would
call them early in the morning to remind them.
And they’d be, ‘Oh my God. I’m so glad you
called because honestly I forgot I had an
appointment.’ (MSW)
Importance of phone communication - I think
the phone calls are important. And I think if I
had to do it over again with some of the patients
I think, what I would do is, if they didn't make it.
And a lot of times I would just call them and
say, okay well let's schedule for next week. I
would just go ahead and do the PST over the
phone. With a lot of them, I didn't do it only
because I had another patient coming or
something you know at the moment. Or maybe
I thought let me do this*. She didn't make it, so
let me go ahead and does this other work
instead of you know well, maybe I could just
take the opportunity to do PST over the phone.
Or catch up with them, how did you do this
week? Did you do any of the homework?
Things like that. But sometimes I couldn't do
that also only because they were - and
somebody was sick. I'm not feeling well.
Couldn't even talk over the phone. Or they
were someplace that they couldn't talk because
it* situation it wasn't an opportune time. But I
think if in the future I would just go ahead and
do it over the phone. (LCSW)
Flexibility - Yeah, because there were a lot of
patients that given their work schedules they’re
like, ‘I just can’t miss. I can’t come in.’ I think
once I did a session like on someone’s lunch
break. While they were away getting their lunch,
they did their session and I was very flexible
with them. And that helped out. I would stay
later or I’d come in really early. I think I had one
patient that would always come in at eight
o’clock. I showed up at seven anyway. So it
was never a real big deal for me, but you know
little things like that. (MSW)
Table 6: Continued
125
Flexibility - I think you certainly have to be
flexible and in a variety of ways. Not only being
able to meet with the patient on certain days,
certain timeframes, but even if it’s on weekends
and trying to make a contact with them because
a lot of our patients maybe working or caring for
kids or whatever. And sometimes they may be
more free on the weekends or in the evenings.
(LCSW)
Recruitment
Strategies:
• Birthday
greetings
• Type of
Incentive
Birthday greetings - You know like in terms of
those birthday cards I would think that, because
I had two people who were not - they were not
coming in that actually called to thank us. Even
know they weren't coming in- somebody sent
them the birthday card. And those two
came….Yeah, just from that day they came.
(MSW)
Birthday greetings - Yeah, even though you
didn't participate we still want to remember you
on your birthday ... In fact, one of the patients
mentioned that she really likes the birthday
cards that we send out…..Like and that’s
something that’s like so simple to do but yet,
you know, I wouldn’t think it would make a
difference. But to them, they really thought that
that was - it showed that they cared and we
weren’t just calling them just to collect data.
(Project Assistant)
Types of Incentive - For money yeah or for
transportation to over here to come to the
appointment because most of them are low
income. Sometimes they have problems with
that because they want the money not the gift
card … I explain it to them and sometimes they
say, ‘Why don’t you give me the $10 cash?’
(Recruiter)
Cultural
Patient-Provider
Language/ Cultural
Matching Strategies
Language Matching - You know I think that
what keeps them is the fact that they feel
comfortable with me because I speak Spanish.
And because I speak Spanish and because I
can translate and help them understand things
that are written or given to them in English, they
feel so comfortable talking with me just like the
phone call I just had. Some lady called me and
Table 6: Continued
126
said, ‘Oh you know I spoke to Sylvia and I
forgot what she told me.’ So I think that what
brings a lot of these men and women to come
back to seek either me or Sylvia is the fact that
we speak Spanish and they feel comfortable
speaking to someone of their native language,
who knows their native language. (Patient
Navigator)
Cultural Matching - I think personalizing the
sessions is probably one of the most important
things that motivated them. It’s kind of like
they’re coming in because they’re excited
because they see the difference or they’re
noticing a change in themselves. And it all
starts with establishing rapport with the patient
from the get go. Things like, ‘Oh where are you
from?’ If they were, for example, from Central
America, ‘Where in Central America? I’ve been
there before.’ And talking about things like
culture, something like food or just saying -
culture has a lot to do with it too. The fact that
you know I share with some of the patients very
specific cultural elements like you know ethics
and morals and religion. All of those things you
know played a big role. And then, almost
trusting me you know it’s kind of like, ‘Well, she
looks kind of like me. She speaks like me. You
know okay.’ And I would kind of push them.
(MSW)
Cultural Matching - I think it’s just cultural. I
think it’s cultural. I think that the minute they
would see you, they would just not want to talk
to you. You know some people have that. If
you’re not Asian, they won’t talk to you. If you’re
not Black, they won’t talk to you. You know if
you’re not white, they won’t talk to you. So it’s
more like if I go in and there is a Hispanic lady,
she knows that I’m Hispanic. She will take the
time to talk to me. But I will go in with an Asian
person and they will kick me out. I go in with the
African-American and will tell me to go. ‘I don’t
have time for you. Get out.’ (Recruiter)
Table 6: Continued
127
Systems
• Patient Systems
Navigation
Strategies
• Provider –
Provider
Rapport and
Communication
Establishing Provider-Provider Rapport – I
make cards. I say, ‘My name is Claudia. I work
for the MDDP.’ And I will put it on the chart.
When the patient, when I knew that that patient
had diabetes, when the nurses would get that
chart, they would see my orange, and it was
orange, bright orange. And I will put it on the
chart and they knew that that patient - I needed
to talk to that patient. And that’s one of the
things that worked over there at the clinic. I
made a bunch of orange cards. I would put
them on every morning. I would go in there
before the clinic would open and put up all my
orange cards that I knew those patients were
diabetic that I wanted to interview … So when
the nurses would get them and after they would
take the vital signs, that’s why I’m saying it’s
very important you get along with the staff
because they will come and tell me, ‘Claudia
you have a patient in Room 3. Claudia you
have a patient in Room 5, Room 6, Room 7.’
because of that orange card. Other than that,
they would not. If you don’t get along with the
staff, they’re not going to help you. They’re not
going to help you. That was one thing that
works for me, for me. (Recruiter)
128
Rapport building as a key retention strategy
Just as patients described overlapping barriers, providers also described
overlapping strategies which can be useful to retaining challenging patients in a clinical
depression treatment trial. With regard to clinical strategies used most by the
MSW/LCSW or the psychiatrist, establishing and building rapport with patients was an
overarching strategy that providers thought should be used to retain potential dropouts.
With sensitivity to patient culture, building clinical rapport was the common thread
throughout discussions about 1) early patient engagement, 2) maintaining patient
contact throughout sessions, and 3) provider persistence in retaining patients in
treatment. Examples of this overlap in provider strategies are provided below.
Two different social work providers described this relationship between building
rapport and the importance of early treatment engagement in retaining patients to
depression treatment:
It's really, really important to have a good connection with your patients from the
very beginning whether it's over the phone or in person. (MSW provider)
…If you don’t develop a sense of rapport or relationship with the client at the
beginning, and that involves various things. That involves you know simple small
talk, just getting to know the patient when you’re doing your initial assessment
and just having that connection with that client. So that if that’s not established at
the get go, then it’s going to be very difficult to go ahead and have the patient
come back to see you. I mean if they’re not feeling comfortable and they don’t
feel like it’s a safe environment for them to sort of share their feelings, intimate
thoughts and feelings, then they’re not going to want to come back. So it’s very
important. (LCSW provider)
One MSW provider also described the importance of the relationship between
building rapport and maintaining on-going contact with the patient: “The follow-up calls,
the in-between calls, the giving the time in session to have that relationship building, I
think really helped them feel engaged.” And another MSW provider described the use of
clinical persistence in establishing rapport with patients: “I think it was very important.
129
And I like to say I think only - I was able to finish my PST with every single one of my
patients except for one. But it was hard. It wasn’t easy, and it took a lot of persistence.
But I think the ones who I connected with well don’t want to say it was me. I think it was
them because I don’t think I was different with my patients.”
Outcome-focused retention strategies
Providers also described the importance of outcome-focused clinical retention
strategies which were not only used to point out patient successes for the patient, but
also to motivate themselves to continue to remain on track by measuring their clinical
effectiveness with monitoring of PHQ-9 depression scores. One MSW provider briefly
described the process:
“From the beginning, tying in their own interests and sort of what is it you want to
accomplish. Or, what the context of therapy is or what brought you into the
therapy….something actually we were told to do which I thought ended up being
a good thing. These patients who were scoring high on their PHQ-9s, we were
told to do in-between visit follow-up calls. And that was I think very helpful ‘cause
it allowed us to check in on, not only their progress and you know catch if there’s
anything more severe coming up or if there are any issues that we needed to be
aware of.”
She went on to describe the importance of how an outcome-focused clinical
approach can influence patients to complete treatment:
I heard patients say very specifically, ‘I see a difference.’ Or, ‘I’m no longer doing
this.’ Or you know it’s part of why I like what I do and I got so much satisfaction
out of working with the patients is that you just see this new look on their face.
You know as they’re moving through the therapy and the excitement that they
have when they see the changes. Even as how small they are, that’s what you
know they want to come back. And also even at the end of a session, the feeling
that the sense of relief that they have at the end of the session because they’ve
been able to give that time to themselves. And that release is what keeps them
coming back and getting that support … So whether it’s very immediate
gratification or they’re seeing their progression over those eight weeks or those
few months where we did follow-ups. That’s what kept them coming back…
This same MSW provider discussed her process of helping the patient to
“partializing” their problems, as a way to remain outcome-directed and motivate her
patients:
130
Well I don’t think I used motivational interviewing in the traditional sense from the
way I understand it and the way that I’ve kind of learned about it. But the way that
it was used in the therapies, the therapy that we did was I think much more like
session by session of what is it going to - for this step, for example, what is it that
you want to get out of this. Or, how are we going to use this to help you get to
that next, that end goal ... So the motivation may have started out in something
very big like I want to lose weight and I want to do all this exercise. But then we
had to break it down and say ‘Okay, well how are you going to do these things?
And then how do you feel about these things?’ And once they accomplished that
or did that next step, reflecting on whether that was a good thing for them and
how it improved their ability to have a sense of accomplishment about something
because part of it they didn’t have a strong sense of accomplishment because
they saw themselves kind of spinning.
Another MSW summed up how an outcome-focused approach helped to inspire
optimism and hope for patients, which can lead to motivation and continued
participation:
To have them recognize that there's change because if they don't see if that
there's any change they are not going to stay in. And so it's like even as minute
as them taking walks or them visiting with the neighbor, if you have to - you have
to constantly show them that there is changes. Because I think when they come
and see that they are all of a sudden be totally cured and it's not that. So I kind
of educate them what therapy is. You know sometimes it's going to be, you're
going to feel worse because you're talking about things that you kept inside. So I
got to like set them up to - it's not going to be like, the skies are going to open
and you're going to feel better. You might go through this feeling down, more
depressed or you might feel - so I think once you kind of like set that up they are
like, okay. So I go, give us a chance. You've been like this for 20 years and
now.
Two MSW providers also described the benefits of tracking clinical outcomes for the
provider:
It definitely kept me on my toes. And also there is an expectation that all of our
patients would finish all of the six to eight sessions. And if they weren’t, we were
asked why and how were we going to problem-solve ourselves to get them to
come back. I think that helped, but it definitely reinforced the need to do it. On
one hand it kind of was like you know I felt at times … Well on one hand I felt
like, oh, I’m just staying them in there because I need to meet my numbers. You
know, but on the other hand, it also helped the patient you know because the
patient was coming in. So at times I felt like, okay I’m being forced to do this
arbitrary six to eight within a certain amount of time and get PHQ9s, a certain
number of PHQ9s within a certain amount of timeframe because I have to report
them and so on, but … On the flip side, you know it pushed me to be very
creative in how I was …Exactly, and how many sessions. How many times I
called them. How many times I check in on them and so on. So I think that I
131
mean it had two sides to it. I definitely think it added a structure for all of us to
push through and make sure that these patients were participating in treatment.
Of course, it's like paperwork, anything. But when I see the end product and
when I kind of like got, contacted this person like a billion times. So when I can
see the outcome it becomes okay. Yeah, getting to the computer and writing it
up is a drag, but thank God that the project made it easy for information and it
wasn't horrible….. So just like similarly to the patient needing to know their
improvement, their change, their tracking it's important for you probably, it
motivates you to also see that what you're doing is working.
Patient Education Strategies in Addressing Informational Barriers
Providers discussed the use of education and conveying knowledge and
information, in addressing many barriers to patients completing their depression
treatment. The most frequently needed forms of patient education within this context
often require additional education about the study and psycho-education about their
depression and mental health. Providers acknowledged barriers which involved
misunderstandings about their informed consent and initial participation in the study.
One MSW provider describes a case in which this occurred: “Of course, you apologize.
You're like, ‘God I'm so sorry that you're not able to remember but we do have your
signature that day that you had signed that day. And this is actually to benefit you and to
really help you with what you might be experiencing.’ And then that's when I go back
and say, ‘Well you know back on this day you had said that you were feeling depressed.’
And ask them, ‘Do you know what depression is?’” Another provider also described her
experience in addressing this problem from her role as a recruiter in the study:
…The consent forms are not grade level reading so they don’t - they kind of
grasp it and understand a little bit, but not exactly what they’re reading. So when
they would read the consent form, there was a lot of questions. You know some
of them didn’t even know how to read. Some of them they would just initial it … I
would read it to them just for the people that didn’t have - didn’t you know, didn’t
know how to read. Those are the ones that I would actually wait for after because
they were… Yeah because they would be - they were interested in participating
with us, but I didn’t know what it exactly said in that consent form. So before even
signing the consent form, I would have to read it for them and for them to
understand it. Then once that, they would just initial it and then that was it … And
I think, I mean, that’s kind of the same thing that I’m talking about like kind of
132
their trust issue. You know that first of all, they’ve never met me and many times
the first question they will ask me is, ‘Are you the person who I talked to at the
hospital?’ And I’ll, thank God on the sheet it says who interviewed them. And I’ll
say, ‘Oh no, that was you know Maria.’ Or, you know whoever. And they’ll say,
‘Oh.’ And I’ll say, ‘Oh she interviewed you and then she gave me your
information and that’s why I’m calling you. And I’m going to be calling you every
six months and asking you these questions.’
This same project recruiter uses a patient education strategy in addressing this
barrier: “And I try to give them some - I try to give them as much information so that they
feel comfortable that I’m not just, you know, anybody off the street calling them. That I
actually kind of know a little bit about them. I know if they have diabetes. I know if they
cancer-whatever it is right?”
Providers also recognized the need for psycho-educational strategies for patients
who were expressing uncertainty about being depressed or their need for depression
treatment. One MSW provider described her experience with this:
I remember this guy said, ‘Well, you know I'm feeling a lot better.’ That comes up
again, it's like I'm feeling a lot better. I feel that I don't really need it. And that's
just re-educating them. I mean that's one of the main things that I have -- just
them saying that they felt a lot better and they didn't need to continue with
treatment …Well, I would just tell them that you know yeah, it's great that they
are feeling a lot better. But in order to prevent relapse and all you need to
complete the treatment, the full eight to ten sessions, the full course you can get.
That's what I do is I just kind of educate them about what depression is again and
how sometimes it comes and goes but it's important to complete the treatment to
prevent it from coming back.
As seen with rapport-building clinical strategies, providers described overlapping
strategies which involved patient education, within the context of motivational strategies.
Patient education was the common thread throughout some discussions about early
engagement and motivational interventions. Various providers described the use of
education to help gain early motivation that could be sustained through completion of
sessions:
I've had a couple patients who are kind of like, ‘Oh my god this is not going to get
better,’ but then again that's when we motivate them is showing them that's okay to
feel that way but it is going to take some time. I think it is just really important just to
133
educate them from the very beginning to let them know that therapy doesn't happen.
The improvement in therapy doesn't happen overnight. And it's going to take some
time to get things better. (MSW Provider)
Well I think if you, as a case manager or therapist, if you call, if you speak with them,
if they’re not making it to their appointments, and you kind of let them know that this
is a - explaining the benefits of their therapy to them and hopefully making them
realize that you know not only will they be healthy themselves, but at the same time,
they’re in a clinical trial so this can probably in the future benefit other people who
are depressed. Maybe laying it out that way and just, I think, a phone call and being
positive with them not necessarily make why, why the why not’s. But better explain to
them the benefits and I think that would motivate them to come in if you better
explain to them. (Patient Navigator)
I think generally speaking, I could say that the interpersonal relationship was very
important for the patients. But the patients who are hard to engage, I had to put a lot
more time into educating them and convincing them about what the therapy was
about and how it could help them … I have to say the ones who are the hardest to
engage it was because I was so persistent that they kept coming back. (MSW
Provider)
Concrete patient navigation and case management strategies
Providers reported strategies which would reduce one of the patients’ most
common barriers - transportation. A study patient navigator briefly reported the same
types of transportation-related barriers: “ … A lot of them don’t even have vehicles to
transport them. But the main thing would be that a lot of their family members work and
they’re all by themselves.”
Although, as part of the ADAPt-C intervention, the provision of transportation
tried to address this common problem, patients and providers still reported transportation
problems continuing to interfere with patient study completion. One MSW also described
transportation and related proximity issues that her patient faced, from her role as a
study clinician: “Yeah, well for me on my end, it has helped. Providing with assistant
transportation has really, really helped. But on the other hand, if we wouldn't provide
that then of course it would be a major barrier. But there are some patients who move
out of the area. And they say you know ‘That's too far for me. I can't continue.’ Or
patients who go out of the country, I think that's another barrier.”
134
In addition to the extra effort on the part of the patient, solutions often also
required more time, close involvement and coordination of team members, and
additional study resources. One LCSW provider describes this common scenario:
And even with Access (Services) [a local transportation service], we got a couple
of people Access (Services), we were able to eventually get them Access
(Services) but you know that was difficult too. And it's hard for them to negotiate
that system because you call and it's a recorded message. And they have a hard
time with that. The case manager would help with that to alleviate some of that
stress for them. And so some of them were able to make it with Access but
again with us, we'd have to wait. So we knew that we couldn't plan. If it was an
afternoon appointment for like a lady, when she had an appointment I think it was
it at two or one. She said ‘I'll finish seeing my husband 1:00; I should be here by
2:00.’ I knew I wasn't going to leave here (the hospital) until seven. Just because
by the time she ended, it was one of those, which was fine …
There was often frustration on the part of the provider in attempting to solve
patients’ transportation problems, while also being mindful of the study resources and
limits on using study funds for transportation. This patient navigator describes such
frustration: “I was told that we could give money for gas, but it wasn’t for every patient
probably because as you know it’s a study and there’s not much money to do that for
every single patient. But it probably would definitely work for some.”
Providers suggested additional solutions to addressing future transportation
problems within the context of a treatment trial. There were a variety of transportation
solution strategies offered by providers: van transportation, someone to pick up the
patient, taxi, money for gas, or bus tokens.
The attempt to address transportation barriers is a perfect example of the
important role that the non-clinician (e.g., Patient Navigator or Case Manager, study
recruiters, and project assistants) play, within the context of a treatment intervention.
These roles are an integral part of the team effort. One MSW describes these pivotal
roles:
135
The case manager, having access to that, knowing that someone is going to
follow up with my patients. So I think that the team that, we have we all work
together … And what helps is like a lot of case managers already know the
patients. When the recruiter was on board we could - what was nice about that is
that I could contact her because she was always downstairs in the clinic, so if a
patient was a no show she would keep her eye out.
In addition to making concrete logistical plans for patient transportation to
appointments, providers consistently acknowledged the need for patient navigators or
case managers to assist with daily study tasks (e.g., appointment reminder calls,
ensuring current contact information is correct, tracking down lost-to-follow-up patients,
and other logical details). Providers described the combination of these tasks which
actually contribute to patients showing up for their appointments and follow-through. The
study psychiatrist said, “Regular and frequent contact with patients is very crucial to
patient compliance with treatment.” Such an effort requires other strategies, in
combination to this one, to actually ensure patient follow through and retention.
Patient and Provider Systems Strategies
Providers often provide advice and information for patients on what to expect
when patients have to encounter systems barriers related to hospital personnel,
bureaucratic, and logistical problems related to scheduling, parking, waiting for
appointments, etc. This MSW provider described very practical strategies that she
shares with patients who have to deal with common systems issues:
That's when I say, you know what when you know you're going to have your
appointment? It's really important, just come prepared. Bring some snacks. And
some say, well you know I really don't have money to bring snacks. I barely had
enough to come to - and that's an issue. Now, that was difficult for me. It's like
good God. It's like how do you deal with that. I go, ‘What happened last night
you were able to come to your appointments. What did you do?’ And she's like,
‘Well I had saved up a little bit more money for that day.’ Or that's a night I tell
them I say ‘Well, try to get here earlier.’ A lot of them are like, ‘I don't like the
system. I don't really like how they treat me.’ I kind of try to empower them. I
say, ‘You know there's always administration. You can always file a compliant
with administration.’ I'm sure they would hear. A lot of them are like ‘Oh, I don't
want to have any problems. I don't want to document it. And I don't want them
to stop my medicines because I filed a complaint.’ And that's when I reassure
136
them. I'm like with the information that you do disclose to them I'm sure it will
held confidential. They’re not going to publicize it. But it's really important for us
to improve our service at LAC + USC that we need to be informed of what is
going on. And I said, in order for things to get better it's really important to talk
about them with someone.
Providers often acknowledged patients’ frustration yet often themselves feel
reluctant and helpless about offering strategies on how best to help patients with these
larger issues, which often prevent retention. One LCSW provider described her
experience in working with such issues:
I think what they tell you particularly with cancer for them I remember it just being
so much time in the clinic and so many appointments. So that there were - it just
seemed like an endless amount of appointments. I mean we had at another
appointment and I think we were pretty down on the priority scale. They had
radiation, chemo and then they had to go have some tests done, and then they
had to go for the medication. It took a long time. And so I think for them it was
just time factor. I think that's partially a systems barrier just because it took so
long for them to negotiate that system. Not through any fault of their own really
but just because the system is what it is.
Providers seemed to have more concrete strategies for addressing their own provider
systems barriers within this large public care sector. A recruiter describes her strategy
for trying to recruit her patients to the study, within a very busy patient population and
clinic staff:
They had the little waiting room. If they were talking to me, they were not paying
attention to me ‘cause they were listening to the overhead and to making sure
that they would get called by the nurse to take their vital signs. So what I would
do is I would let one of the nurses know, ‘He’s with me or she’s with me. You
know I’m interviewing him right now. So if you’re going to call him, let me know.
I’ll bring him in.’ … So we had like I had a strategy you know. Then they knew
me. That’s the thing you have to make friends with the people you’re working
with. That’s another thing …they wouldn’t let me know. If I had the patient, they
would be like, ‘Oh I need him to take vital signs.’ I would follow the patient and
once he was done with the vital signs, I would follow the patient to the room and
that’s how I went along to doing my whole interview.
In summary, providers agreed that there were feasible strategies that providers
could implement to address patient barriers, and increase engagement and retention
among low-income minority cancer patients to depression treatment.
137
Chapter 6: Discussion
Summary of Aims
The goal of this study was to shed light on the barriers and factors associated
with depression treatment dropout among low-income minority cancer patients who had
agreed to participate in a randomized clinical depression treatment trial. Additionally, this
study sought to identify strategies that could be used by study trial providers and
clinicians to increase retention in depression treatment in a public care system. This
study’s sequential aims were to:
1. Explore (via individual telephone interviews) 20 low-income, minority cancer
patients’ perspectives about depression treatment, their reasons for dropping out
of treatment, and barriers to treatment adherence;
2. Compare these dropout patients’ perspectives and barriers to the perspectives of
10 low-income, minority cancer patients who completed depression treatment,
while also eliciting (through individual telephone interviews) the latter group’s
enablers of treatment;
3. Understand the barriers contributing to dropout, from providers’ perspectives (by
abstracting provider clinical notes which describe possible barriers and
obstacles); and
4. Identify (via provider face-to-face and telephone interviews) viable study provider
strategies to decrease dropout and increase retention in treatment.
Summary of Findings
With regard to the first aim, the findings indicate that patients who dropped out of
treatment (“dropouts”) perceived a number of barriers that interfered with their treatment
completion: cancer-related (which included the emotional impact of the cancer diagnosis
[n=20] and cancer treatment commitments [11]), depression treatment-dissatisfaction
138
[n=9], informational (which included study misunderstandings [n=6]), instrumental (which
included transportation problems [n=14], financial issues [n=11], employment-related
concerns [n=11], and caregiving demands [n=3]), cultural (which included language
communication problems [n=7] and discrimination from providers [n=5]), and systems-
related (which included service-related logistical issues [n=13] and patient-provider
problems [n=10]). These barriers were not mutually exclusive; instead they were often
described in combination, i.e., patients described “multiple confounding barriers” that
interfered with the continuation of depression treatment.
Findings from the second aim reveal that “completers” also experienced “multiple
confounding barriers” but were able to continue and complete treatment. Potential
explanations for this discrepancy can be found within some of the discrete barriers and
associated narratives themselves, as dropouts individually reported experiencing more
of every type of the above barriers, as compared to completers. Completers also were
able to identify enablers or facilitators to their completion of treatment: engaging or
connecting with their providers (n=9, 90%), getting “advice” from their therapist (n=7,
70%), and the scheduling flexibility (n=3, 30%) offered by their provider, some of which
are offered as suggested strategies in previous literature (Coday, et al., 2005,
Ogrodniczuk, et al., 2005; Tutty, 2005). The additional unique beneficial strategy of
“discussing cancer” was seen as a personalized way of meeting the completers’
immediate needs for discussion and dialogue.
With regard to the third aim, Andersen & Newman’s (2005) social and individual
determinants of medical care utilization provide specificity to the categorization of
possible reasons and factors which contribute to patient dropout. Analyses of providers’
dropout clinical notes reveal that there are contributing factors related to Predisposing
Social Structure factors (which included seeking work, work time conflicts, caregiving
139
demands, “housing instability,” moving/ proximity problems, lacking social support, job
stress, and transportation problems), Enabling Family factors (which included family
financial problems), Enabling Community factors (which included changing providers,
dissatisfaction with hospital staff and medical services), and Perceived Illness Level
(which included health concerns, “chemo treatment,” and “pain” severity). Findings
unique to this data reveal that there were additional factors, which included Enabling
Family factors (different from those “Enabling Family factors” above, but which included
“conflict with family,” poor family communication, and family “cancer attitudes”) and
Additional Enabling Psychological Coping factors (e.g. “feeling better,” “not interested,”
“emotional stress,” grief over a “loss,” panic attacks, and anxiety over desired
pregnancy).
The fourth aim focused on provider strategies for increasing engagement and
retention among low-income minority cancer patients with depression. Specifically,
findings indicated that providers can generate feasible strategies that address some of
the various patient-identified barriers: 1) Depression treatment strategies, which included
patient satisfaction surveys, efforts to strengthen the therapeutic alliance (e.g., building
rapport, early engagement, and active listening), and clinical motivation strategies (e.g.,
persistence, consistency, outcome-focused counseling, validation, family involvement,
and strength identification); 2) Informational strategies, which included education about
the study and psycho-educational strategies); 3) Instrumental strategies, which included
transportation resources, consistent contact, reminder calls, phone communication, and
flexibility); 4) Recruitment strategies, which included birthday greetings and the
importance of incentive types, 5) Cultural strategies, which included patient-provider
cultural and language matching; and 6) Systems’ strategies, which included patient
140
systems navigation strategies and the importance of provider-provider rapport and
communication.
This chapter will explore the theoretical implications of the findings, research
design implications, clinical social work implications, limitations of the study, and future
community-based and research directions.
Theoretical Implications
Socio-cultural, psychosocial, provider, and health system factors’ influence on patient
dropout were explored using individual patient interviews, secondary data analysis of
provider notes, and provider interviews. Such an approach allowed for a systematic and
triangulated organization of findings, while reflecting on existing theoretical constructs. A
systematic construction of adherence categorization dimensions was first useful in the
development of a conceptual model to think categorically about medical adherence and
follow-up, in promoting health prevention and treatment programs (Logan & Freeman,
2000). Findings reveal that the classic work of Meichenbaum & Turk’s (1987) systematic
models of medical adherence framework provided a good starting point to thinking about
possible ways to organize the ecological characteristics of dropout, but is limited for this
study of low-income, predominately Latina immigrants. Meichenbaum & Turk’s (1987)
systematic models of medical adherence framework included: 1) characteristics of the
patient, 2) characteristics of the treatment regimen, 3) features of the disease, 4) the
relationship between the health care provider and the patient, and 5) the clinical setting.
This limited perspective conceals important family, cultural, and political influences
(within the “characteristics of the patient”) on the decision to participate and continue
with treatment. For example, providers described several family-related barriers in their
clinical notes (e.g., family financial problems, “conflict with family,” poor family
communication, and family “cancer attitude”), which seemed to indirectly influence
141
patient discontinuation of treatment. Literature suggests that adherence should be
investigated in relation to family and cultural beliefs, perspectives, and meanings (Vega
et al., 2007). If we were to use Meichenbaum & Turk’s model to categorize the barriers,
we would have to include these particular family-related barriers within the
“characteristics of the patient”, which blurs the importance of family influences.
Andersen and Newman’s (2005) model of health use offers a more useful heuristic for
categorizing the dropout and adherence factors, distinguishing the family, and other
societal, health services system, and individual factors. For theory to help drive
interventions, it must focus attention on how to select the important factors we can
influence from among many factors associated with behavior (Montano & Kasprzyk,
2002).
Similarly, the Theory of Planned Behavior health behavior model also provided a
good beginning conceptualization to important influences (e.g., barriers, perspectives on
depression and depression treatment, and support influences) on depression treatment
continuation decision-making. Although these areas are extremely important, we see
that for this diverse population, there are weighty ethnic, cultural, and political influences.
Appraisals of stressors and coping responses are often rooted in cultural experiences
and may vary with minority and socioeconomic status (Montano & Kasprzyk, 2002).
Although TPB proved a good starting point, it was limited in its focus on micro- and
meso-level systems’ categories to describe influences and the focus on “emotion” or
feeling factors (e.g. “how do you feel about …”, “what are your thoughts about ….”).
Although cultural barriers were somewhat abstract and difficult to articulate at times, for
this study population, the “multiple confounding barriers” and individual variables might
also have been influenced by non-predominant environmental, political/organizational,
and cultural factors (e.g. immigration laws and constraints, Medi-cal eligibility rules,
142
family support in another country, language issues). These macro-level systems’
influences are critical for consideration with this population.
A solution to this gap in TPB can be feasibly considered using the Integrated
Behavior Model (IBM) (Kasprzyk, Montano, & Fishbein, 1998), an extension to the TPB
model which essentially reconfigures and adds to the TPB and has elements from
Health Belief Model, Social Cognitive Theory, and the Transtheoretical Model. The
predictors in the IBM have evolved from the TPB, but with slightly new construct names
which predict the intention to perform a behavior: Attitudes, Perceived norms, and
Personal agency (Kasprzyk, Montano, & Fishbein, 1998). Four new components are
added alongside "intention," as additional proximal predictors of the final behavior:
Knowledge and Skills to perform the behavior, Salience of the behavior, Environmental
constraints, and Habit (Kasprzyk, Montano, & Fishbein, 1998). Based on the findings of
this study, the IBM Knowledge constraints, Salience of the behavior, and Environmental
constraints, capture several components that the TPB did not include. For this
population, future constructs to consider would include primary elements of pre-existing
motivation and self-efficacy, which could be added to the model to help determine the
likelihood to participate and continue treatment. Future research should focus on actual
determinants and facilitators of regular health behavior within a theoretical framework
that incorporates cultural, ethnic, and socioeconomic diversity (Green McDonald, P. et
al., 1999).
Research Design Implications
Prior to discussing the clinical social work implications, it is important to first
explore the study research design implications, which will include discussion of sample
characteristics, recruitment dynamics, incentives, interviewing considerations, and the
importance of maintaining contact.
143
Sample Characteristics
It is important to consider briefly whether some of the dropouts’ demographic
characteristics played any role in their discontinuation of treatment. In terms of
immigration status, dropouts had been in the United States longer than completers (80%
versus 50% in the US 10+ years, respectively), which could possibly suggest that
completers were not yet as acculturated and were therefore more likely to comply with
treatment. Interestingly, in terms of education, dropouts were more educated than
completers (40% versus 70% had less than a high school education, respectively).
Educational level comparisons are particularly interesting given that studies of dropouts
in other populations, mainly White, report that a significant dropout correlate includes
patients who have lower education (Garfield, 1994; Nakao, 2001), which is different from
this sample of low-income, minority cancer dropouts. Since this study did not look
statistically at demographic dropout correlates for this population and previous cancer
studies did not look at these correlates, future quantitative studies should be conducted.
Interestingly, the groups generally did not differ on psychiatric issues: except both were
experiencing lower anxiety levels, yet both groups had moderate clinical depression
levels. This is interesting given that typically there is a co-morbidity between major
depression and anxiety. No dropouts were receiving medication or a combination of
medication and Problem Solving Treatment (PST), which is consistent with their
generally negative perceptions of antidepressant medication.
Recruitment Dynamics
This study demonstrates that even the most challenging patients (e.g. patients
who drop out of treatment) can be reached, recruited, and retained in a research study,
as all dropout patients who were contacted agreed to participate in this study even
though they had discontinued participation in depression treatment. This successful
144
recruitment may have been related to the gift card incentive, the convenience of the
telephone interview, and/or that the patient remembered the study staff who requested
the interview, from prior interaction in the parent study. Despite the successful
recruitment and data collection with the dropout patients, as noted in Chapter 4, not all
interviews were easy to conduct. Through debriefing, study staff were able to categorize
a pattern of some respondents as “challenging.” In many of these interviews,
interviewers offered gentle motivating comments, which were effective in encouraging
patients to continue with the interview. Clinicians who work with populations that are
challenging and hard-to-reach should incorporate elements of motivational interviewing
techniques to assist them with their recruitment efforts.
Dropout skepticism and reluctance over the research study protections in
returning the medical release forms point to important clinical research implications.
Although this fear of medical research has been well-documented in previous clinical
trial recruitment studies (Alvidrez & Arean, 2002; Corbie-Smith et al, 1999; Freedman,
1998; Freeland & Isaacs, 2004; Hussain-Gambles et al., 2004; Moreno-John et al.,
2004), this is particularly interesting given that dropout patients agreed to participate in
initial telephone interviews, where they were audio-taped. There was more skepticism
about agreeing to have clinicians look in their medical records than with recording the
interview. Perhaps patients felt more control over the information they would provide
over the phone, but were concerned about allowing access to “confidential medical
records.” These patients may not have been willing to take the chance. Equally
interesting are “passive refusal” patients who had verbally agreed to return the medical
release upon multiple requests and telephone/ mailed written reminders, but did not
return them. This may reflect an element of just pleasing the provider over the telephone
(which may be influenced by cultural factors), or perhaps the interference of life priorities
145
and demands got in the way of follow-through. It would be interesting to find out if the
likelihood of follow-through for the 5 non-participating dropouts would increase if the
patient received more than a $5 incentive or if there was more rapport between the
interviewer and patient. This question presents many implications for future research in
cognitive processes in decision-making and the heuristics of making health decisions.
Considering Incentives
Incentives have been shown in the literature to play an important role in the
recruitment of hard-to-reach patient populations to clinical research interventions and
practice. The majority of dropout participants chose a Food 4 Less over a Target gift
card, pointing to the importance of having sample-relevant incentives and a choice of
incentives. Although some patients actually preferred cash over a gift card incentive, the
incentive amount had to be high enough to show that their participation was valued and
respected. With ethical human subject considerations, clinicians and researchers need
to be cognizant of the desired types and value of incentives, especially when trying to
recruit and retain hard-to-reach patients.
Interview Subtleties
Despite literature which suggests “dropout” as the most common term for non-
adherence, there were dropouts in this study who did not like the “dropout” label, and
denied dropping out of treatment. It is difficult to precisely give the number of dropouts
who denied dropping out of treatment because some of them qualified this by saying that
they were somewhat confused about whether they had discontinued. Despite confusion
for some, 8 dropout patients did assert at some point in the interview that they had not
dropped out. Some patients even became somewhat defensive and upset with this label
during the interview. Given this reaction during the initial interviews and the higher
chance of non-participation for this group of patients, the decision was made mid-stream
146
by the interviewers to adjust the interview guide and refer to them as “decliners,” which
seemed to be a less stigmatizing term. (Despite the shift in terminology for data
collection purposes, the term “dropout” is utilized in this presentation of data because it
is a more common term in the literature and an accurate term for what these patients
technically did, e.g. “drop out” of treatment). They did not decline participation, but rather
discontinued treatment (e.g., dropped out) after agreeing to participate.
As for why some patients did not consider themselves to have dropped out,
perhaps patients got all they needed out of the treatment and were satisfied with the
treatment they received; even though from a research perspective they dropped out of
treatment, from a phenomenological perspective, they completed what they needed to
complete. Another reason may have been that some patients may not have been able to
remember the sequence of events leading up to their discontinuation. There might have
also been Spanish translational issues, with the term “dropout” not being directly
translatable. Also, some patients may have, in fact, been currently in treatment with
another provider so they may have perceived themselves to still be in treatment, even
though they were not technically in the treatment program to which they had consented.
Due to the potentially negative and/or stigmatizing connotation of the term (e.g. “high
school dropout”), it may be important in future studies to choose alternative terminology
or to at least approach patients who discontinued treatment with a more exploratory
approach regarding their decisions.
Additionally, this study, in part, attempted to understand how culture and ethnicity
influence decision-making about whether to continue participation in depression
treatment. However, when interviewers probed for these types of cultural, gender, and
ethnicity responses, both dropout and completer patients did not understand these
questions. Topics related to culture, gender, and ethnicity are more subtle and
147
unconscious concepts, and are thus difficult to understand and reflect upon personally.
Generally, people do not naturally reflect on how their culture/ethnicity or gender impacts
their health behavior. So, analyses required that this P.I. and other project assistants
attempt to identify interview instances where these issues were apparent. There were
none found.
Maintaining Contact
Maintaining patient contact throughout the study is critical to clinical practice and
research participation. About half (n=19) of the sample pool (n=39) were ultimately not
reachable due to relocation or telephone number issues. Multiple recruitment attempts
(e.g., contacting alternate numbers, numerous reminder mailings to contact, phone call
reminders, voicemails, no shows or showing up late to appointments, checking directory
assistance/the yellow pages or patient medical charts, meeting at MD appointments)
required a considerable amount of provider and systems resources and time. The only
uncontrollable factor in patient contact is death (two cases); the remaining 17 were lost
due to ostensibly controllable factors. More frequent concerted efforts could be
undertaken to ensure accurate patient contact information. As suggested by providers,
clinicians and recruiters can extend more reach to regular frequent contact and calls.
This not only helps develop the rapport between the patient and provider, but also
ensures that patient information is current and up-to-date. As mentioned, there are a
variety of feasible methods to maintaining contact. This is not only important for RCT
research trials, but also social work and clinical practice.
Clinical Social Work Implications - Barriers and Strategies
The major finding of this study was the “multiple confounding barriers” that
seemed to contribute to some patients dropping out of treatment. As was identified with
most barriers, it is often difficult to extract the sole reason for the patient not continuing
148
with treatment. Barriers overlapped and often influenced one another. For example,
language-related barriers and perceived discriminatory barriers were often described as
combined experiences, as well as dissatisfaction with depression treatment and patient
and provider issues. This multiplicity of barriers often created the sense that there were
indirect barrier links, instead of strong direct links. This multiplicity of factors suggests
that intensive case management may be useful early in treatment participation to
address pressing instrumental issues. Perhaps if these issues are effectively addressed
early on (as suggested by providers), patients will be more likely to remain in treatment
and benefit from behavioral treatment.
Multiple confounding barriers were not unique to the dropout patients, as 60% of
both dropouts and completers described overlapping barriers. However, something
about the completer sample facilitated their completion of treatment. It could be asked
whether the completers were more highly motivated than the dropouts for some reason.
Although motivation is somewhat of an abstract concept, it is important to consider. A
more specific question might be: what moves and motivates some patients to remain in
treatment and what are the best ways to address the barriers that impede some patients
from remaining in treatment? Clinical strategies should incorporate more intentional
motivational strategies. In addition, this might speak to a future need for a prospective
study in which everyone who enrolls is followed equally to see who stays in and who
drops out of treatment and why. Motivational interviewing seems to be relevant at all
stages of the clinical intervention (recruitment to post-treatment follow-up and relapse
prevention), both in clinical practice settings and research.
Multiple domain barriers often involved the need for multiple domain strategies to
address these barriers. Within one narrative, patients identified a constellation of barriers
which simultaneously contributed to them dropping out of treatment. In reflecting back to
149
Table 3 (page 99), there were several barriers within and among the different types of
barriers. Just as important to analyze were the cells that are empty: e.g some barriers
were not identified through this study. For 4 patients, there were no identifiable
informational, cultural, or systems barriers which interfered with their continuation of
depression treatment. This lack of identification of barriers may suggest that the
interview protocol did not tap into these patients’ experiences, or that the patients
themselves were unable to articulate their reasons for discontinuation. Again, a
prospective study would lend itself well to a more phenomenological understanding of
discontinuation and completion.
In addition, it is important to help patients navigate the demanding world of
cancer treatment commitments. Cancer treatment barriers can create a disruption to
daily life routine (Wells & Turney, 2001), especially in a large public sector care system
where there are often long waiting periods for appointments. The timeliness involved
with this can have a rippling effect into other life commitments and things that patients
want to participate in, e.g. depression treatment. The issue of multiplicity of barriers
becomes clear when discussing cancer treatment barriers, as patients are experiencing
the aggressiveness of the treatment regimen, side effects experienced, general physical
health, disruption to routine and lifestyle, level of understanding about the treatment
plan, age and point in the life cycle, tolerance of uncertainty, problem-solving capacity,
relationship with the health care team, and availability of social support will collectively
influence the ability of each person to manage the rigors of therapy (Wells & Turney,
2001). Given that cancer treatment barriers can affect those in both active treatment and
those who require additional medical appointments with doctors, it is especially
important that providers and clinicians offer flexibility (Young & Maher, 1999) including:
rescheduling appointments when the patient is not feeling well or misses appointments;
150
arranging late afternoon and weekend appointments to accommodate patients or family
members on whom the patient is dependent for transportation to the clinic , and finding a
private available room within the busy, often noisy clinic setting
(Ell, et al., 2007), which
may facilitate treatment retention.
Relevant to the discussion of cancer-related barriers is the impact of comorbid
medical problems interfering with treatment follow through. Patients not only described
the impact of their cancer diagnosis and treatment commitments, but their overall health
and how comorbid medical problems were affecting them. Although these problems may
not have directly impacted their decisions to dropout from depression treatment, they
may be areas of clinical consideration and significance in treating these patients.
Comorbid chronic diseases are common in persons with cancer, and the prevalence of
comorbidity has important clinical, health service, and research implications (Ogle,
Swanson, Woods, & Azzouz, 2000). Social workers working with cancer patients need to
be prepared to work with other psychosocial issues which relate to comorbid illnesses.
In comparing completer-described enablers and provider-described retention
strategies, there was agreement about the importance of offering “advice,” clinical
engagement between therapist and patient, and flexibility in scheduling and seeing their
assigned therapist and provider. For example, there was agreement that “advice” about
a variety of personal matters was beneficial. In fact, dropouts even mentioned the
benefits of “advice”-giving which was gained from their informal supports. “Advice” and
information seemed to offer a sense of security and reassurance in a likely state of
confusion. This is especially important given the often uncertain nature of the cancer
disease and treatment continuum. Another enabler which was described as important by
both completers and providers was the importance of rapport or engagement, which is
necessary to the sustainability of the treatment. Engagement was also seen as an asset-
151
building strategy, in the way that it secures the connection and relationship development
between the patient and provider. Providers strategized that this engagement should be
especially directed to earlier sessions, where there is greater likelihood of dropout
(Ogrodniczuk et al., 2005). Flexibility was established by patients and providers as
critical to navigating many of the patients’ instrumental barriers, thus helping to sustain
them to treatment. Targeted flexibility to accommodate patient scheduling preferences
(Tutty, Ludman, & Simon, 2005) as a retention strategy is consistent with the literature
for this hard-to-retain population where there are many work-related barriers and
practical matters which often need special scheduling considerations.
What providers did not recognize that was identified by completers as contributing
to retention was the importance of “discussing cancer.” Given the magnitude of
emotional impact of a cancer diagnosis, as described by both completers and dropouts,
it is extremely important to address the related psychosocial concerns that arise. The
fact that providers did not mention this strategy might suggest some discomfort on the
part of the provider to discuss such issues, or they might feel that this discussion falls
within the purview of the physicians. Social workers might first be focusing on patients’
practical problems, however a part of their role should involve a general understanding
of how psychosocial issues are affected within the cancer continuum, and its relationship
to the constraints of problem solving. Although recent national attention on psychosocial
oncology (National Institute of Health State of the Science Panel, 2004; National Institute
of Mental Health, 2002) has shed light on this topic, this has not been sufficiently
funneled into evidence-based psychosocial practice strategies in working with cancer
patients. The clinical social work strategy of “starting where the patient is” in offering
therapy which is relevant and useful to a patient should include discussion of the illness,
152
if this is needed by the patient. This strategy can be applied to other acute and chronic
illnesses, where there is a serious psychosocial impact.
Completers said that discussion of cancer was useful to their retention.
Accordingly, the dropout data was carefully examined to see if there was any mention of
this from dropouts. Although they did not specifically mention the desire for more
discussion about issues related to their cancer diagnosis, one Spanish-speaking Latina
patient said that having more of an understanding of what she was “going through at that
moment…would’ve helped” her. Dropouts were not specifically probed about their
interest in having more information, so it is possible that this was more important to
them, despite what the data suggests.
Depression Perspectives
Dropout patients discussed their perceptions of antidepressant medications as
part of the rationale for their preference of counseling over medication. No dropouts
were enrolled with an ADAPt-C study psychiatrist. Some of these dropout patients also
had a “good for others, but not for me…” attitude, and some were concerned that they
might become addicted to antidepressants. When patients were asked if they thought
medication could be beneficial for others like themselves, they reported that they thought
it could. However when asked about their feelings about antidepressant medication for
themselves, they often did not think it was necessary. Patients hold many beliefs about
their health and about the potential efficacy of any proposed treatment action
(Meichenbaum & Turk, 1987). Although some patients’ rationales for not liking
antidepressants were based on real past experiences (e.g., side effects), there were
others who had stigmatizing thoughts about antidepressant medication (e.g., they are for
“crazy people” or they are “addictive”). These findings strongly suggest the need for
psycho-educational approaches during the early cognitive phases of treatment. This
153
would be useful in addressing some of these negative antidepressant perceptions. In
addition, almost half of patients were dissatisfied with depression treatment. This makes
sense, in that they did not believe in the effectiveness of treatment, which suggests that
evaluation should take place throughout the treatment study in assessing patient
satisfaction (as suggested by providers).
With regard to patient dissatisfaction, patients generally did not feel entitled to
complain or express their medical or health-related questions to their providers. This
might reflect a role of “passive compliance” and “just doing what provider says.” Miranda
(2004) shows that Latinos’ engagement in treatment is often related to the concept of
respect. This “people-pleaser” role may be even more pronounced among Latinas
(females). This is important clinically in that clinicians may need to reassure patients that
it is acceptable to express their dissatisfaction or disappointment about depression
treatment, as well as with other treatment. This concept may have also been evident
with the “passive refusers,” who agreed to return their medical releases, but did not
return them.
Similarly, the study misunderstanding barriers are important to recognize
clinically, but are also potentially related to issues of whether consent to participate in
treatment was truly “informed” and other ethical implications for human subjects’
research. It is unknown whether the reasons for this are related to language translation,
educational level, health literacy, or the combination of factors.
Support
Patients were resourceful enough to choose or call upon a religious or spiritual
support, so they would not have to rely on informal support persons, not necessarily
because informal support did not exist. This finding shows that the existence of informal
support networks is sometimes inadequate, and additional support might be necessary.
154
Findings are consistent with literature which says that the quality, not quantity, of support
influences the individual’s ability to cope with distress and adhere to treatment. Although
all dropouts identified more informal supports than completers, half unfortunately
experienced the inability to depend upon and use these informal supports for a variety of
reasons. On the other hand, although completers identified less informal support (80%),
they had less of an inability to use their informal support (20%). So, during times of
crisis, it is important to have an identified source or surrogate support system in place. In
addition, dropout patients indicated that they rely heavily upon themselves. We should
not discourage reliance on self, yet empower patients on how to appropriately cope and
deal with their problems independently, while also strengthening other support networks
(as backup). We should also encourage spiritual and religious supports when they are
desired and relevant to patients’ lives. Providers expressed that family involvement
would strengthen the likelihood that patients would continue, but rarely involved family
members. This should be taken on a case-by-case basis, as families do not always
provide appropriate or sufficient support. Providers can do a better job at helping
patients to identify a sustainable, supportive network.
Dropouts described more reliance on spiritual support, which might speak to a
characteristic element of fatalism or feelings of passivity or powerlessness. In fact, 30%
of dropouts felt they had an inability to overcome these barriers, whereas none of
completers felt this same powerlessness. This finding is important due to strong
evidence that optimism, self-efficacy, and ease in coping with lifestyle changes are
largely influenced by patients’ sense of self-efficacy (Rapley & Fruin, 1999). Future
research could focus on the connections among some of these characteristics. For
example, patients who had combined “interview challenges” (difficulty engaging during
telephone interview) and “medical release reluctance” are important clinically because
155
perhaps if we can identify challenges or anxiety and reasons for hesitation early on
within the interview or assessment (as suggested by providers), we might perhaps be
able to address this by delivering better engagement and motivation strategies early on
in treatment and encouraging better adherence, follow-through, and completion of
treatment. In addition, patients who describe the use of religion for support and who
conveyed “passive compliance” (“just doing what the doctors said to do...”), perhaps
reflect a dimension of fatalism which affects health care decision-making. It could be
important to use a psycho-educational component of the intervention to help these
patients draw on the empowering and assertive strengths of their religion to assist with
health behavior decisions. Social workers could also address the earlier cognitive
restructuring stages and address “irrational” beliefs, which would require spending more
time in the earlier cognitive treatment stages to combine the emotional reactions and
rational thinking, inherent in CBT and PST.
Clinicians in all walks of health care are still puzzled by those patients who fail to
follow apparently logical, but nonnegotiable, assignments or prescriptions when they are
clearly designed to improve health status, detect or reduce health-risk behavior, and
combat disease (Brawley and Culos-Reed, 2000; Delgado, 2000). Interestingly, the
parent study accounted for many of the instrumental barriers by providing for
transportation, therapy and/or medication being offered at no cost to the patient, provider
flexibility in scheduling treatment appointments (on weekends and evening hours
commonly offered to patients), and childcare being offered at the hospital for free. Yet,
these issues were highlighted by dropouts as playing a role in their discontinuation of
treatment. Were patients aware of these services? And if not, why? Were they not in
treatment long enough for the provider to have informed them about these services?
Had they received sufficient help? Clinicians and researchers alike are left wondering:
156
how can a patient who has been offered free medication and depression counseling,
transportation, childcare not uptake this opportunity? What more could clinicians and
researchers do to address instrumental barriers, thus retaining patients to treatment?
Study Limitations
Despite the many strengths of this study design, and the study recruitment and
clinical lessons learned, this study has several important limitations which need to be
considered in future similar research. First, these patient interviews were retrospective in
nature by one to two years. At times, there was somewhat poor recall and abstract
recollection of the obstacles of dropout and facilitators of depression treatment
completion. Second, interview challenges may have created a limited volume and
quality of information from each patient, thus weakening the ability to gaining in-depth
perspectives. However, when conducting qualitative research, it is reasonable to expect
that there will just be some interviews that are easier and better than others. Third, since
most of these interviews were bilingual, some meaning was lost at times due to Spanish-
English language translation. However, bilingual interviewers checked the translated
Spanish transcripts that they each conducted, for accuracy. Fourth, since the focus of
the completer guide was not oriented toward the barriers encountered, there might have
been a minimization of the barriers, thus decreasing barrier frequencies. As expected,
dropouts might have indicated more barriers to completion because they were probed
more for these. They also were not probed for facilitators to the same extent as
completers because they had not completed treatment, but they could have been asked
more about what might have contributed to completion of treatment. Fifth, it is important
to remember that these patients were all participating in a clinical trial that attempted to
address barriers. As such, most low-income patients not engaged in such trials
undoubtedly experience more barriers, both more of those identified in this study and
157
more that were not identified. Finally, with regard to the last phase of the study, there
was limited provider representativeness, as there was only one psychiatrist interviewed.
In addition, this psychiatrist only provided written responses, which did not allow for the
spontaneity inherent in narrative responses and verbal dialogue.
Future Community-based and Research Implications
Extending clinical findings and knowledge, this study has laid the groundwork for
future community-based engagement and retention intervention research with low-
income, minority depressed cancer patients and others who experience similar types of
chronic illness. Existing socio-culturally tailored intervention strategies benefit only those
who participate and remain in treatment. What about those individuals and communities
that we do not hear from and who do not continue with their recommended treatment?
What are the needs and barriers that these groups face? An equally important question
is: What moves and motivates individuals and their communities to uptake and remain in
recommended treatment? When we think about health and mental health
communication delivery, we need to understand what individuals and communities know
about their illness and disease, what they want, and their preferred mechanisms for
health education, communication, and delivery. We not only need to find new, innovative
ways to reach individuals who miss their primary health and mental health clinical
appointments, but we also need to find ways to reach groups who are not yet in contact
with these systems. It becomes important to integrate strategies geared toward enrolling
hard-to-reach populations in treatment and helping them remain in treatment. When we
begin to think about ideas for addressing sustainability in providing clinical and
community-based psychosocial services, the professions of social work and public
health begin to converge.
158
Emerging research solutions lead down a path to exploring ways to encourage
adherence and increase retention through more community-based intervention
approaches and health communication mechanisms to reach and move communities
and networks of individuals. In maintaining a commitment to social justice for all, thinking
needs to be expanded beyond individual intervention strategies, to those which take on
a more targeted rippling effect outward beyond the patient, and into the family and
community dimensions. It is not only important to think about individual health behavior,
but also strategies that motivate groups and communities of people. In addition, given
that health behavior is multi-factorial, the approaches that health care clinicians use
should take a collaborative and holistic approach to improving access and health
outcomes. For example, it may be important to identify a trusted member of a patient’s
family or community, who would be able to be an ambassador for patient behavior
change. Effective intervention strategies and solutions might be found at the
intersections of social work and public health, with the delivery vehicle being a tested
health communication approach (e.g., printed manuals and pamphlets, narrative and
educational videos, culturally tailored social marketing, the use of ethnically and socially
embraced celebrity spokespersons, effective provider-patient psycho-education). In
addition, clinical and community social work can benefit from better use of technology
and health communication to address psychosocial problems, particularly with health
literacy and breakdowns in provider-patient communication, perhaps by use of tele-
health applications and community problem-solving group visits.
Although there will always be patients who decide to drop out of treatment for
many different reasons (after initially agreeing to treatment), further research in the area
of treatment adherence does bear important attention. Retention improvements can and
should be made. For example, clinical and clinical research efforts should target patient
159
understanding (e.g. informed consent issues) and treatment dissatisfaction. By looking
at patterns of dropout barriers and valuing the reasons behind them, we can begin to
design better targeted adherence and motivational interventions. We can also use these
as models for other illness domains and with different hard-to-reach populations
(socioeconomically and politically vulnerable groups, rural communities, and other
groups who have experienced past historical injustices). Not only should we strongly
consider individual clinical strategies, but we should also extend this knowledge to
population-based adherence strategies in the area of cancer prevention and control.
Such strategies should also include cancer policy initiatives.
In light of new scientific technology, clinical trials, customized medications, and
the mapping of the Human Genome, it is important to think about issues related to
increasing participation and retention in primary health and mental health treatment,
particularly for vulnerable and hard-to-reach populations. Due to historical injustices
particularly for Native American Indians and African Americans, there are rippling effects
seen today in the way of stigma, suspicion, and mistrust, which often create highly
challenging barriers to participation and retention in clinical research and interventions.
This is especially important for low-income, minorities, and other vulnerable groups
because much of the knowledge from new scientific technology is not getting funneled
into communities who experience the worst cancer morbidity and mortality rates. More
community-based approaches will help to echo a louder call to policy on behalf of
individuals who are not accessing and receiving benefits of new scientific technology.
160
References
Acosta, F. X. (1980). Self-described reasons for premature termination of psychotherapy
by Mexican American, Black American, and Anglo-American patients.
Psychological Reports, 47, 435-443.
Aday, L. A., & Andersen, R. (1974). A framework for the study of access to medical care.
Health Services Research, 9(3), 208-220.
Akechi, T., Okuyama, T., Akizuki, N., Azuma, H., Sagawa, R., Furukawa, T. A., et al.
(2006). Course of psychological distress and its predictors in advanced non-small
cell lung cancer patients. Psycho-Oncology, 15, 463-473.
Alvidrez, J., & Arean, P. (2002). Psychosocial treatment research with ethnic minority
populations: Ethical considerations in conducting clinical trials. . Ethics &
Behavior, 12(1), 103-116.
Alvidrez, J., Azocar, F., & Miranda, J. (1996). Demystifying the concept of ethnicity for
psychotherapy researchers. Journal of Consulting and Clinical Psychology,
64(5), 903-908.
Andersen, R., & Newman, J. F. (1973). Societal and individual determinants of medical
care utilization in the United States. Milbank Memorial Fund Quarterly 51(1), 95-
124.
Andersen, R. M., & Newman, J. F. (2005). Societal and individual determinants of
medical care utilization in the United States. Milbank Quarterly, 83(4), 1-28.
Andrykowski, M. A., & Manne, S. L. (2006). Are psychological interventions effective and
accepted by cancer patients? I. Standards and levels of evidence. Annals of
Behavioral Medicine, 32(2), 93-97.
Antoni, M. H., Lehman, J. M., Kilbourn, K. M., Boyers, A. E., Culver, J. L., Alferi, S. M., et
al. (2001). Cognitive-behavioral stress management intervention decreases the
prevalence of depression and enhancs benefit finding among women under
treatment for early-stage breast cancer. Health Psychology 20(1), 20-32.
Araya, R., Rojas, G., Fritsch, R., Gaete, J., Rojas, M., Simon, G., et al. (2003). Treating
depression in primary care in low-income women in Santiago, Chile: a
randomised controlled trial. The Lancet, 361(9362), 995-1000.
Archer, J., Hutchison, I., & Korszun, A. (2008). Mood and malignancy: Head and neck
cancer and depression. Journal of Oral Pathology & Medicine, 37, 255-270.
Arean, P. A., Ayalon, L., Hunkeler, E., & et al. (2005). Improving depression care for
older, minority patients in primary care. Med care, 43(4), 381-390.
Arean, P. A., & Gallagher-Thompson, D. (1996). Issues and recommendations for the
recruitment and retention of older ethnic minority adults into clinical research.
Journal Consulting and Clinical Psychology, 64(5), 875-880.
161
Auslander, W., & Freedenthal, S. (2006). Social work and chronic disease: Diabetes,
heart disease, and HIV/AIDS. In S. Gehlert & T. A. Browne (Eds.), Handbook of
health social work (pp. 532-567). Hoboken, NJ: John Wiley & Sons, Inc.
Ayalon, L., Arean, P. A., & Alvidrez, J. (2005). Adherence to antidepressant medications
in black and Latino elderly patients. The American Journal of Geriatric Psychiatry
13, 572-580.
Ayres, A., Hoon, P. W., Franzoni, J. B., Matheny, K. B., Cotanch, P. H., & Takanyanagi,
S. (1994). Influence of mood adjustment to cancer on compliance with
chemotherapy among breast cancer patients. Journal of Psychosomatic Rex,
38(5), 393-402.
Bailey, R. K., Geyen, D.J., Scott-Gurnell, K., Hipolito, M.M.S., Bailey, T.A., & Beal, J.M.
(2005). Understanding and treating depression among cancer patients.
International Journal of Gynecological Cancer, 15, 203-208.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change.
Psychological Review, 84(2), 191-215.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ:
Prentice Hall.
Bandura, A. (1994). Self-efficacy. In V. A. Ramachaudran (Ed.), Encyclopedia of human
behavior (Vol. 4, pp. 71-81). New York: Academic Press. (Reprinted in H.
Friedman [Ed.], Encyclopedia of mental health. San Diego: Academic Press,
1998).
Barsevick, A. M., Sweeney, C., Haney, E., & Chung, E. (2002). A systematic qualitative
analysis of psychoeducational interventions for depression in patients with
cancer. Oncology Nursing Forum, 29(1), 73-87.
Bernal, M. W., & Castro, F. G. (1994). Are clinical psychologists prepared for service and
research with ethnic minorities? Report of a decade of progress. American
Psychologist, 49, 797-805.
Blumer, H. (1954). What is wrong with social theory? American Sociological Review(18).
Boeije, H. (2002). A purposeful approach to the constant comparative method in the
analysis of qualitative interviews. Quality & Quantity, 36, 391-409.
Bowen, D. J., Cartmel, B., Barnett, M., Goodman, G., & Omenn, G. S. (1999). Predictors
of participant retention in two chemoprevention feasibility trials. Annals of
Behavioral Medicine, 21(3), 210-215.
Brawley, L. R., & Culos-Reed, S. N. (2000). Studying adherence to therapeutic regimens
overview, theories, recommendations. Controlled Clinical Trials, 21(5), S156-
S163.
162
Breitbart, W. (1995). Identifying patients at risk for, and treatment of major psychiatric
complications of cancer. Support Care Cancer, 3, 45-60.
Brown, D. R., & Topcu, M. (2003). Willingness to participate in clinical treatment
research among older African Americans and Whites. The Gerontologist 43(1),
62-72.
Burton, M. V., Parker, R.W., Farrell, A., Bailey, D., Conneely, J., Booth, S., & Elcombe,
S. (1995). A randomized controlled trial of preoperative psychological preparation
for mastectomy. Psycho-Oncology, 4, 1-19.
Cabassa, L. J., & Hansen, M. C. (2007). A systematic review of depression treatments in
primary care for Latino adults. Research on Social Work Practice, 17, 494-503.
Cabassa, L. J., Zayas, L.H., and Hansen, M. (2006). Latino adults' access to mental
health services: A review of epidemiological studies. Administration and Policy in
Mental Health and Mental Health Services Research 33(3), 316-330.
Campbell, R., Pound, P., Pope, C., Britten, N., Pill, R., Morgan, M., et al. (2003).
Evaluating meta-ethnography: A synthesis of qualitative research on the
experiences of diabetes and diabetes care. Social Science Medicine, 56, 671-
684.
Castro-Blanco, D. R. (2005). Cultural Sensitivity in Conventional Psychotherapy: A
comment On Martínez-Taboas. Psychotherapy Theory, Research, Practice,
Training, 42(1), 14-16.
Cella, D. F., Tulsky, G., Sarafian, B., Linn, E., Bonomi, A., Silberman, M., et al. (1993).
The functional assessment of cancer therapy scale: development and validation
of the general measure. Journal of Clinical Oncology, 11(3), 570-579.
Chen, S. W., & Davenport, D. S. (2005). Cognitive-behavioral therapy with Chinese
American clients: Cautions and modifications. Psychotherapy: Theory, Research,
Practice, Training, 42(1), 101-110.
Christensen, A. J., & Johnson, J. A. (2002). Patient adherence with medical treatment
regimens: An interactive approach. Current Directions in Psychological Science,
11(3), 94-97.
Chyun, D. A., Amend, A. M., Newlin, K., Langerman, S., & Melkus, G. D. (2003).
Coronary heart disease prevention and lifestyle interventions. The Journal of
Cardiovascular Nursing, 18(4), 302-318.
Classen, C., Butler, L.D., Koopman, C., Miller, E., DiMiceli, S., Giese-Davis, J., Fobair,
P., Carlson, R.W., Kraemer, H.C., & Spiegel, D. (2001). Supportive-expressive
group therapy and distress in patients with metastatic breast cancer. Archives of
General Psychiatry, 58, 494-501.
163
Coday, M., Boutin-Foster, C., Goldman Sher, T., Tennant, J., Greaney, M. L., Saunders,
S. D., et al. (2005). Strategies for retaining study participants in behavioral
intervention trials: Retention experiences of the NIH Behavioral Change
Consortium. Annals of Behavioral Medicine, 2(55-65).
Comas- Díaz, L. (1981). Effects of cognitive and behavioral group treatment on
depressive symptomatology of Puerto Rican women. Journal of Consulting and
Clinical Psychology, 49(5), 627-632.
Cooper, L. A., Hill, M. N., & Powe, N. R. (2002). Designing and evaluating interventions
to eliminate racial and ethnic disparities in health care. Journal of General
Internal Medicine, 17, 477-486.
Corti, L., & Bishop, L. (2005). Strategies in teaching secondary analysis of qualitative
data. Forum: Qualitative Social Research, 6(1).
Coulehan, J. L., Schulberg, H. C., Block, M. R., Madonia, M. J., & Rodriguez, E. (1997).
Treating depressed primary care patients improves their physical, mental, and
social functioning. Archives of Internal Medicine, 157, 1113-1120.
Coyne, J. C., Lepore, S. J., & Palmer, S. C. (2006). Efficacy of psychosocial
interventions in cancer care: Evidence is weaker than it first looks. Annals of
Behavioral Medicine, 32(2), 104-110.
Creswell, J. W., & Maietta, R. C. (2002). Qualitative Research. In D. C. Miller & N. J.
Salkind (Eds.), Handbook of research design and social measurement (6 ed.).
Thousand Oaks: CA: Sage.
Davis, L., Evans, S., Fishman, B., Haley, A., & Spielman, L. A. (2004). Predictors of
attrition in HIV-positive subjects with peripheral neuropathic pain. Aids Care,
16(3), 395-402.
Delgado, P. L. (2000). Approaches to the enhancement of patient adherence to
antidepressant medication treatment. Journal of Clinical Psychiatry, 61(Suppl. 2),
6-9.
Denzin, N. K. (1978). The research act: A theoretical introduction to sociological
methods. (2 ed.). New York: McGraw Hill.
Derogatis, L., Morrow, G., Fetting, J., Penman, D., Piasetsky, S., Schmale, A., et al.
(1983). The prevalence of psychiatric disorders among cancer patients. Journal
of American Medical Association, 249(6), 751-757.
Deshields, T., Tibbs, T., Fan, M. Y., & Taylor, M. (2006). Differences in patterns of
depression after treatment for breast cancer. Psycho-Oncology, 15, 398-406.
DiMatteo, M. R., Lepper, H.S., Croghan, T.W. (2000). Depression is a risk factor for
noncompliance with medical treatment. Archives of Internal Medicine, 160, 2101-
2107.
164
Dwight-Johnson, M., Ell, K., & Jiuan-Lee, P. (2005). Can collaborative care address the
needs of low-income Latinas with comorbid depression and cancer? Results
from a randomized pilot study. Psychosomatics, 46(3), 224-232.
Edelman, S., Lemon, J., Bell, D.R., & Kidman, A.D. (1999). Effects of group CBT on the
survival time of patients with metastatic breast cancer. Psycho-Oncology, 8, 474-
481.
Ell, K., Quon, B., Quinn, D. I., Dwight-Johnson, M., Wells, A., & Lee, P. (2007).
Improving treatment of depression among low-income patients with cancer: The
design of the ADAPt-C study.General Hospital Psychiatry, 29, 223-231.
Ell, K., Wells, A., Nedjat-Haiem, F., Lee, P. J., & Vourlekis, B. (2008). Economic stress
among low-income women with cancer: Effects on quality of life. Cancer, 112(3),
616-625.
Ersoy, M. A., Noyan, A. M., & Elbi, H. (2008). An open-label long-term naturalistic study
of mirtazapine treatment for depression in cancer patients. Clinical Drug
Investigation, 28(2), 113-120.
Fann, J. R., Thomas-Rich, A. M., Katon, W. J., Cowley, D., Pepping, M., McGregor, B.
A., et al. (2008). Major depression after breast cancer: A review of epidemiology
and treatment. General Hospital Psychiatry 30(2), 112-126.
Fawcett, J. (1995). Compliance: definitions and key issues. Journal of Clinical
Psychiatry, 56(Suppl. 1), 4-8.
Figueira, M. L., & Ouakinin, S. (2008). From psychosomatic to psychological medicine:
what's the future? Current Opinion in Psychology, 21(4), 412-416.
Fisch, M. J., Loehrer, P. J., Kristeller, J., Passik, S., Jung, S. H., Shen, J., et al. (2003).
Fluoxetine versus placebo in advanced cancer outpatients: a double-blinded trial
of the Hoosier Oncology Group. Journal of Clinincal Oncology, 21(10), 1937-
1943.
Fishbein, M. (Ed.). (1967). Readings in attitude theory and measurement. New York:
Wiley.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction
to theory and research. Readin, MA: Addison-Wesley.
Fouad, M. N., Partridge, E., Wynn, T., Green, B. L., Kohler, C., & Nagy, S. (2001).
Statewide Tuskegee alliance for clinical trials. Cancer, 91, 237-241.
Fukui, S., Kugaya, A., Okamura, H., Kamiya, M., Koike, M., Nakanishi, T., Imoto, S.,
Kanagawa, K., & Uchitomi, Y. (2000). A psychosocial group prevention for
japanese women with primary breast carcinoma: A randomized controlled trial.
Cancer, 89(5), 1026-1036.
165
Gany, F., Leng, J., Shapiro, E., Abramson, D., Motola, I., Sheild, D. C., et al. (2007).
Patient satisfaction with different interpreting methods: A randomized controlled
trial. Journal of General Internal Medicine 22(Suppl. 2), 312-318.
Garfield, S. L. (1963). A note on patients' reasons for terminating therapy. Psychological
Reports, 13, 38.
Garfield, S. L. (1994). Chapter 6: Research on client variables in psychotherapy. In
Handbook of psychotherapy and behavior change (pp. 190-228). New York:
Wiley, J.
Geertz, C. (1973). The interpretation of cultures. New York: Basic Books.
Gehlert, S., & Browne, T. A. (Eds.). (2006). Handbook of health social work. Hoboken,
NJ: John Wiley & Sons, Inc.
Gil, K. M., Wilson, J. J., Webster, D. A., Abrams, M. A., Orringer, E., Grant, M., et al.
(1996). Effects of cognitive coping skills training on coping strategies and
experimental pain sensitivity in Afridan American adults with sickle cell disease.
Health Psychology, 15(1), 3-10.
Gilbar, O., & Neuman, R. (2002). Which cancer patient completes a psychosocial
intervention program? Psycho-Oncology, 11, 461-471.
Given, C., Given, B., Rahbar, M., Jeon, S., McCorkle, R., Cimprich, B., et al. (2004).
Does a symptom management intervention affect depression among cancer
patients: Results from a clinical trial. Psycho-Oncology, 13, 818-830.
Givens, J. L., Datto, C. J., Ruckdeschel, K., Knott, K., Zubrittsky, C., Oslin, D. W., et al.
(2006). Older patients aversion to antidepressants: A qualitative study. Journal of
General Internal Medicine, 21, 146-151.
Glantz, K., Rimer, B. K., & Lewis, F. M. (Eds.). (2002). Health behavior and health
education (3rd ed). San Francisco, CA: Jossey-Bass.
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Stragegies for
qualitative research. New York: Aldine de Gruyter.
Goodwin, P. J., Leszcz, M., Ennis, M., Koopmans, J., Vincent, L., Guther, H., et al.
(2001). The effect of group psychosocial support on survivial in metastic breast
cancer. The New England Journal of Medicine, 345, 1719-1727.
Green, L. W., Richard, L., & Potvin, L. (1996). Ecological foundations of health
promotion. American Journal of Health Promotion, 10(4), 270-281.
Harman, J. S., Edlund, M.J., Fortney, J.C., & Kallas, H. (2005). The influence of
comorbid chronic medical conditions on the adequacy of depression care for
older Americans. Journal of the American Geriatric Society, 53, 2178-2183.
166
Heaton, J. (2008). Secondary analysis of qualitative data: An overview. Historical Social
Research, 33(3), 33-45.
Heppner, P. P., Witty, T. E., & Dixon, W. A. (2004). Problem solving and human
adjustment: A review of 20 years of research using the Problem Solving
Inventory. The Counseling Psychologist, 32, 344-428.
Hilderbrandt, M. G., Steyeberg, E. W., Stage, K. B., Passchier, J., & Kragh-Soerensen,
P. (2003). Are gender difference important for the clinical effects of
antidepressants? The American Journal of Psychiatry, 160, 1643-1650.
Hochbaum, G. M. (1958). Public participation in medical screening programs: A
sociopsychological study. Washington, D. C.: Government Printing Office.
Holland, J. C., Romano, S. J., Heilingenstein, J. H., Tepner, R. D., & Wilson, M. G.
(1998). A controlled trial of fluoxetine and desipramine in depressed women with
advanced cancer. Psychooncology, 7(4), 291-300.
Hopko, D. R., Bell, J. L., Armento, M., Robertson, S., Mullane, C., Wolf, N., et al. (2008).
Cognitive-behavior therapy for depressed cancer patients in a medical care
setting. Behavior Therapy 39, 126-136.
Hopko, D. R., Bell, J. L., Armento, M. E. A., Hunt, M. K., & Lejuez, C. W. (2005).
Behavior therapy for depressed cancer patients in primary care. Psychotherapy:
Theory, Research, Practice, Training, 42(2), 236-243.
Huang, F. Y., Chung, H., Kroenke, K., Delucchi, K. L., & Spitzer, R. L. (2006). Using the
patient health questionnaire-9 to measure depression among racially and
ethnically diverse primary care patients. Journal of General Internal Medicine, 21,
547-552.
Institute of Medicine. (2008). Cancer care for the whole patient: Meeting psychosocial
health needs. Washington, D. C.: The National Academies Press.
Johansson, H., & Eklund, M. (2006). Helping alliance and early dropout from psychiatric
out-patient care. Social Psychiatry and Psychiatric Epidemiology, 41(2), 140-147.
Karasz, A. (2005). Cultural differences in conceptual models of depression. Social
Science & Medicine, 60, 1625-1635.
Kasprzyk, Montano, & Fishbein, (1998). Application of an integrated behavioral model to
predict condom use: a prospective study among high HIV risk groups. Journal of
Applied Social Psychology, 28(17), 1557-1583.
Katon, W., Robinson, P., Von Korff, M., Lin, E. H., Bush, T., Ludman, E., et al. (1996). A
multifaceted intervention to improve treatment of depression in primary care.
Archives of General Psychiatry, 53, 924-932.
167
Khouzam, H. R., Montiero, A. J., & Gerken, M. E. (1998). Remission of cancer
chemotherapy-induced emesis during antidepressant therapy with nefazondone.
Psychosomatic Medicine, 60, 89-91.
Kim, E. Y.-K., Bean, R. A., & Harper, J. M. (2004). Do general treatment guidelines for
Asian American families have applications to specific ethnic groups? The case of
culturally-competent therapy with Korean Americans. Journal of Marital and
Family Therapy, 30(3), 359-372.
King, N., & Ross, A. (2003). Professional identities and interprofessional relations:
Evaluation of collaborative community schemes. Social Work in Health Care,
38(2), 51-72.
Kleinmann, A., Eisenberg, L., & Good, B. (1978). Culture, illness and care: Clinical
lessons from anthropologic and cross-cultural research. Annals of Internal
Medicine, 88, 251-258.
Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief
depression severity measure. Journal of General Internal Medicine, 16(9), 606-
613.
Kuijer, R. G., Buunk, B. P., DeJong, G. M., Ybema, J. F., & Sanderman, R. (2004 ).
Effects of the brief intervention program for patients with cancer and their
partners on feelings of inequity, relationship quality and psychological distress.
Psycho-Oncology, 13, 321-334.
Legato, M. J., Gelzer, A., Goland, R., Ebner, S. A., Rajan, S., Villagra, V., et al. (2006).
Gender-specific care of the patient with diabetes: Review and recommendations.
Gender Medicine, 3(2), 131-158.
Lenze, E. J., Miller, M. D., Dew, M. A., Martire, L. M., Mulsant, B. H., Begley, A. E., et al.
(2001). Subjective health measures and acute treatment outcomes in geriatric
depression. International Journal of Geriatric Psychiatry, 16, 1149-1155.
Lepore, S. J., & Coyne, J. C. (2006). Psychological interventions for distress in cancer
patients: A review of reviews. Annals of Behavioral Medicine, 32(2), 85-92.
Lesser, I. M., Leuchter, A. F., Trivdei, M. H., Davis, L. L., Wisniewski, S. R.,
Balasubramani, G. K., et al. (2007). Insured and non-insured depressed
outpatients: How do they compare? Annals of Clinical Psychology, 19(2), 73-82.
Leventhal, H., Diefenbach, M., & Leventhal, E. A. (1992). Illness cognition: using
common sense to understand treatment adherence and affect cognition
interactions. Cognitive Therapy and Research, 16(2), 143-164.
Lewis-Fernández, R., Das, A. K., Alfonso, C., M.M., W., & Olfson, M. (2005). Depression
in US Hispanics: Diagnostic and management considerations in family practice.
The Journal of the American Board of Family Practice 18(4), 282-296.
168
Lin, E. H. B., Von Korff, M., Katon, W., Bush, T., Simon, G. E., Walker, E., et al. (1995).
The role of the primary care physician in patients' adherence to antidepressant
therapy. Med Care, 33(1), 67-74.
Lincoln, T. M., Rief, W., Hahlweg, K., Frank, M., Von Witzleben, I., Schroeder, B., et al.
(2005). Who comes, who stays, who profits? Predicting refusal, dropout, success
and relapse in a short intervention for social phobia. Psychotherapy Research,
15(3), 210-225.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills: CA: Sage
Maliski, S. L., Kwan, L., Krupski, T., Fink, A., Orecklin, J. R., & Litwin, M. S. (2004).
Confidence in the ability to communicate with physicians among low-income
patients with prostate cancer. Urology, 64, 329-334.
Marchioro, G., Azarello, G., Checchin, F., Perale, M., Segati, R., Sampognaro, E., et al.
(1996). The impact of psychological intervention on quality of life in non-
metastatic breast cancer. European Journal of Cancer, 32A(9), 1612-1615.
Marlatt, G. A., & Gordon, J. R. (1985). Relapse prevention: Maintenance strategies in
the treatment of addictive behaviors. New York: NY: Guilford Press.
Massie, M. J. (2004). Prevalence of Depression in Patients With Cancer. J Natl Cancer I
Monographs. 32, 57-71.
Mathibe, L. J. (2007). Drop-out rates of cancer patients participating in longitudinal
RCTs. Contemporary Clinical Trials, 28(4), 340-342.
McArdle, J. M., George, W. D., McArdle, C. S., Smith, D. C., Moodie, A. R., Hughson, A.
V. M., et al. (1996). Psychological support for patients undergoing breast cancer
surgery: A randomised study. BMJ, 312, 813-816.
McClure, J. B., Catz, S. L., & Brantley, P. J. (1999). Early appointment adherence
among persons living with HIV. AIDS and Behavior, 3(2), 157-165.
McCracken, C. F. M., Boneham, M. A., Copeland, J. R. M., Williams, K. E., Wilson, K.,
Scott, A., et al. (1997). Prevalence of dementia and depression among elderly
people in Black and ethnic minorities. The British Journal of Psychiatry,
1717(269-273).
McDaniel, J. S., Musselman, D. L., Porter, M. R., Reed, D. A., & Nemeroff, C. B. (1995).
Depression in patients with cancer: Diagnosis, biology, and treatment. Archives
of General Psychiatry, 52, 89-99.
McDonough, E. M., Boyd, J. H., Varvares, M. A., & Maves, M. D. (1996). Relationship
between psychological status and compliance in a sample of patients treated for
cancer of the head and neck. Head & Neck, 18(3), 269-276.
169
McLachlan, S.-A., Allenby, A., Matthews, J., Wirth, A., Kissane, D., Bishop, M., et al.
(2001). Randomized trial of coordinated psychosocial interventions based on
patient self-assessments versus standard care to improve the psychosocial
functioning of patients with cancer. Journal of Clinical Oncology, 19(21), 4117-
4125.
Meichenbaum, D., & Turk, D. C. (1987). Facilitating treatment adherence: A
practitioner's guidebook. New York, NY: Plenum Press.
Meissner, W. W. (1996). The therapeutic alliance. New York: NY: Yale University Press.
Meyerowitz, B. E., Formenti, S. C., Ell, K. O., & Leedham, B. (2000). Depression among
Latina cervical cancer patients. Journal of Social and Clinical Psychology, 19(3),
352-371.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis. (2 ed.). Thousands
Oaks: CA: Sage Publishing.
Miller, D. K., Chibhall, J. T., Videen, S. D., & Duckro, P. N. (2005). Supportive-affective
group experience for persons with life-threatening illness: Reducing spiritual,
psychological, and death-related distress in dying patients. Journal of Palliative
Medicine, 8(2), 333-343.
Miranda, J., Azocar, F., Organista, K., Munoz, R., & Lieberman, A. (1996). Recruiting
and retaining low-income Latinos in psychotherapy research. Journal of
Consulting and Clinical Psychology, 64(5), 868-874.
Miranda, J., Azocar, F., Organista, K. C., Dwyer, E., & Arean, P. A. (2003). Treatment of
depression among impoverished primary care patients from ethnic minority
groups. Psychiatric Services, 54(2), 219-225.
Miranda, J., Chung, J. Y., Green, B. L., & et al. (2003). Treating depression in
predominantly low-income young minority women: A randomized controlled trial.
Journal of the American Medical Association, 290(1), 57-65.
Miranda, J., Duan, N., Sherbourne, C. D., Schoenbaum, M., Lagomasino, I., & Wells, K.
B. (2003). Improving care for minorities: Can quality improvement intervention
improve care and outcomes for depressed minorities? Health Services Research,
38, 613-630.
Miranda, J., & Muñoz, R. (1994). Intervention for minor depression in primary care
patients. Psychosomatic Medicine, 56, 136-142.
Montano, D. E., & Kasprzyk, D. (2002). The theory of reasoned action and the theory of
planned behavior. In R. Glanz, B.K., Lewis, F.M., (Ed.), Health behavior and
health education. (pp. 67-98). San Francisco: Jossey-Bass.
Mor, V., Laliberte, L., Morris, J. N., & Wiermann, M. (1984). The Karnofsky Performance
Status Scale. An examination of its reliability and validity in a research setting.
Journal of Clinical Oncology, 2, 1170-1176.
170
Moreno-John, G., Gachie, A., Fleming, C. M., Nápoles-Springer, A., Mutran, E., &
Manson, S. M. (2004). Ethnic minority older adults participating in clinical
research: Developing trust. Journal of Aging and Health, 16(5), s93-s123.
Morrow, G. R., Hickok, J. T., Roscoe, J. A., Raubertas, R. F., Andrews, P. L. R., Flynn,
P. J., et al. (2003). Differential effects of paroxetine on fatigue and depression: A
randomized, double-blinded trial from the University of Rochester Cancer Center
Community Clinical Oncology Program. Journal of Clinical Oncology, 21(24),
4635-4641.
Moynihan, C., Bliss, J.M., Davidson, J., Burchell, L., & Horwich, A. (1998). Evaluation of
adjuvant psychological therapy in patients with testicular cancer: Randomised
controlled trial. Behavioral Medicine Journal, 316, 429-435.
Muñoz, R. F., & Ying, Y. T. (1993). The prevention of depression: Research and
practice. Baltimore, MD: The Johns Hopkins University Press.
Musselman, D. L., Lawson, D. H., Gumnick, J. F., Manatunga, A. K., Penna, S.,
Goodkin, R. S., et al. (2001). Paroxetine for the prevention of depression induced
by high-dose interferon alfa. New England Journal of Medicine, 334(13), 961-
966.
Mynors-Wallis, L. M., Gath, D. H., Lloyd-Thomas, A. R., & Tomlinson, D. (1995).
Randomised controlled trial comparing problem sovling treatment with
amitriptyline and placebo for major depression in primary care. BMJ, 310, 441-
445.
Nakao, M., Fricchione, G., Myers, P., Zuttermeister, P.C., Barsky, A., Benson, H. .
(2001). Depression and education as predicting factors for completion of a
behavioral medicine intervention in a mind/body medicine clinic. Behavioral
Medicine, 26(4), 177-184.
National Institute of Health State of the Science Panel. (2004). State of the Science
Conference Statement: Symptom management in cancer: pain, depression, and
fatigue. Journal of the National Cancer Institute Monograph, 32, 9-16.
National Institute of Mental Health. (2002). Depression and cancer: NIMH Depression
Publications.
Nezu, A. M., C.M., N., S.H., F., & et al. (1998). A problem solving approach: Helping
cancer patients cope. Washington: American Psychological Association.
O'Hair, D., Villagran, M. M., Wittenberg, E., Brown, K., Ferguson, M., Hall, H. T., et al.
(2003). Cancer survivorship and agency model: Implications or patient choice,
decision making, and influence. Health Communication, 15(2), 193-202.
Ogrodniczuk, J. S., Joyce, A. S., & Piper, W. E. (2005). Strategies for reducing patient-
initiated premature termination of psychotherapy. Harvard Review Psychiatry, 13,
57-70.
171
Okamura, M., Akizuki, N., Nakano, T., Shimizu, K., Ito, T., Akechi, T., et al. (2008).
Clinical experience of the use of pharmacological treatment algorithm for major
depressive disorder in patients with advanced cancer. Psycho-Oncology, 17,
154-160.
Olfson, M., Marcus, S. C., Tedeschi, M., & Wan, G. J. (2006). Continuity of
antidepressant treatment for adults with depression in the United States.
American Journal of Psychiatry, 163, 101-108.
Onitilo, A., Nietert, P., & Egede, L. (2006). Effect of depression on all-cause mortality in
adults with cancer and differential effects by cancer site. General Hospital
Psychiatry, 28(5), 396-402.
Padgett, D. (1998). Qualitative methods in social work research: Challenges and
rewards. Thousands Oaks; CA: Sage Publications.
Pampallona, S., Bollnin, P., Tibaldi, G., Kupelnick, B., & Munizza, C. (2004). Combined
pharmacotherapy and psychological treatment for depression: A systematic
review. Archives of General Psychiatry, 61, 714-719.
Pandey, M., Sarita, G. P., Devi, N., Thomas, B. C., Hussain, B. M., & Krishnan, R.
(2006). Distress, anxiety, and depression in cancer patients undergoing
chemotherapy. World Journal of Surgical Oncology, 4(68).
Pasquini, M., & Biondi, M. (2007). Depression in cancer patients: a critical review.
Clinical Practice and Epidemiology in Mental Health 3(2).
Patton, M. Q. (2002). Qualitative research and evaluation methods (3 ed.). Thousand
Oaks: CA: Sage.
Pekarik, G. (1992). Posttreatment adjustment of clients who drop out early vs. late in
treatment. Journal of Clinical Psychology, 48(3), 379-387.
Penedo, F. J., Traeger, L., Dahn, J., Molton, I., Gonzalez, J. S., Schneiderman, N., et al.
(2007). Cognitive behavioral stress management intervention improves quality of
life in Spanish monolingual Hispanic men treated for localized prostate cancer:
Results of a randomized controlled trial. International Journal of Behavioral
Medicine, 14(3), 164-172.
Perez Foster, R. (2007). Treating depression in vulnerable urban women: A feasibility
study of clinical outcomes in community service settings. American Journal of
Orthopsychiatry, 77(3), 443-453.
Petersen, R. W., & Quinlivan, J. A. (2002). Preventing anxiety and depression in
gynaecological cancer: A randomized controlled trial. British Journal of Obstetrics
and Gynaecology, 109, 386-394.
172
Pierce, R., Chadiha, L. A., Vargas, A., & Mosley, M. (2003). Prostate cancer and
psychosocial concerns in African American men: Literature snythesis and
recommendations. Health & Social Work, 28(4), 302-312.
Pinto, B. M., & Floyd, A. (2008). Theories underlying health promotion interventions
among cancer survivors. Seminars in Oncology Nursing, 24(3), 153-163.
Pirl, W. F., Temel, J. S., Billings, A., Dahiln, C., Jackson, V., Prigerson, H. G., et al.
(2008). Depression after diagnosis of advanced non-small cell lung cancer and
survival: A pilot study. Psychosomatics, 49(3), 218-224.
Prochaska, J. O. (1979). Systems of psychotherapy: A transtheoretical analysis.
Homewood: IL: Dorsey Press.
Raison, C. L., & Miller, A. H. (2003). Depression in Cancer: New developments
regarding diagnosis and treatment. Biological Psychiatry, 54, 283-294.
Rapley, P. & Fruin, D. (1999). Self-efficacy in chronic illness: The juxtaposition of
general and regimen-specific efficacy. International Journal of Nursing Practice,
5, 209-215.
Rawl, S. M., Given, B. A., Given, C. W., Champion, V. L., Kozachik, S. L., Barton, D., et
al. (2002). Intervention to improve psychological functioning for newly diagnosed
patients with cancer. Oncology Nursing Forum, 29, 967-975.
Rawl, S. M., Given, B.A., Given, C.W., Champion, V.L., Kozachik, S.L., Barton, D.,
Emsley, C.L., & Williams, S.D. (2002). Intervention to improve psychological
functioning for newly diagnosed patients with cancer. Oncology Nursing Forum,
29(6), 967-975.
Razavi, D., Allilaire, J.-F., Smith, M., Salimpour, A., Verra, M., Desclaux, B., et al.
(1996). The effect of fluoxetine on anxiety and depression symptoms in cancer
patients. ACTA Psychiatrica Scandinavica, 94, 205-210.
Reece, M. (2003). HIV-related mental health care: Factors influencing dropout among
low-income, HIV positive individuals. AIDS Care, 15(5), 707-716.
Reich, M., Lesur, A., & Perdrizet-Chevallier, C. (2008). Depression, quality of life and
breast cancer: A review of the literature. Breast Cancer Research and Treatment,
110, 9-17.
Reis, B. F., & Brown, L. G. (1999). Reducing psychotherapy dropouts: Maximizing
perspective convergence in the psychotherapy dyad. Psychotherapy, 36(2), 123-
136.
Rejeski, W. J., Brawley, L. R., McAuley, E., & Rapp, S. (2000). An examination of theory
and behavior change in randomized clinical trials. Controlled Clinical Trials, 21,
164S-170S.
173
Richardson, J. L., & Sanchez, K. (1998). Compliance with cancer treatment. In J. C.
Holland (Ed.), Psycho-Oncology (pp. 67-76). New York: Oxford University Press.
Rodin, G., & Voshart, K. (1986). Depression in the medicaly ill: An overview. American
Journal of Psychiatry, 143(6), 696-705.
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude
change. Journal of Psychology, 91, 93-114.
Roscoe, J. A., Morrow, G.R., Hickok, J.T., Mustian, K.M., Griggs, J.J., Matteson, S.E.,
Bushunow, P., Qazi, R., & Smith, B. (2005). Effect of paroxetine hydrochloride
(Paxil) on fatigue and depression in breast cancer patients receiving
chemotherapy. Breast Cancer Research and Treatment, 89, 243-249.
Ruskin, P. E., Silver-Aylaian, M., Kling, M. A., Reed, S. A., Bradham, B. D., Hebel, J. R.,
et al. (2004). Treatment outcomes in depression: Comparison of remote
treatment through telepsychiatry to in-person treatment. American Journal of
Psychiatry, 161, 1471-1476.
Sahler, O. J. Z., Fairclough, D. L., Phipps, S., Mulhern, R. K., Dolgin, M. J., Noll, R. B., et
al. (2005). Using problem-solving skills training to reduce negative affectivity in
mothers of children with newly diagnosed cancer: Report of a multisite
randomized trial. Journal of Consulting and Clinical Psychology, 73, 272–283.
Sandgren, A. K., McCaul, K.D., King, B., O'Donnell, S., & Foreman, G. (2000).
Telephone therapy for patients with breast cancer. Oncology Nursing Forum,
27(4), 683-688.
Schneider, M. G., & Chiriboga, D. (2005). Associations of stress and depressive
symptoms with cancer in older Mexican Americans. Ethnicity & Disease, 15, 698-
704.
Schraufnagel, T. J., Wagner, A. W., Miranda, J., & Roy-Byrne, P. P. (2006). Treating
minorty patients with depression and anxiety: What does the evidence tell us?
General Hospital Psychiatry, 28, 27-36.
Sharpe, M., Strong, V., Allen, K., Rush, R., Postma, K., Tulloh, A., et al. (2004). Major
depression in outpatients attending a regional cancer centre: Screening and
unmet treatment needs. British Journal of Cancer, 90, 314-320.
Shea, S. C. (2006). Improving medication adherence: How to talk with patients about
their medication. Philadelphia, PA: Lippencott Williams & Wilkins.
Sher, I., McGinn, L., Sirey, J. A., & Meyers, B. (2005). Effects of caregivers' perceived
stigma and causal beliefs on patients' adherence to antidepressant treatment.
Psychiatric Services, 56, 564-569.
Simon, A. D., Levine, J. L., Lustman, P. J., & Murphy, G. E. (1984). Patient attrition in a
comparative outcome study of depression: A follow up report. Journal of Affective
Disorders, 6, 163-173.
174
Simon, G. E. (2002). Evidence review: efficacy and effectiveness of antidepressant
treatment in primary care. General Hospital Psychiatry, 24, 213-224.
Simon, G. E., VonKorff, M., Wagner, E.H., & Barlow, W. (1993). Patterns of
antidepressant use in community practice. General Hospital Psychiatry, 15, 309-
408.
Simons, A. D., Levine, J.L., Lustman, P.J., Murphy, G.E. (1984). Patient attrition in a
comparative outcome study of depression. Journal of Affective Disorders, 6, 163-
173.
Sivesind, D., & Baile, W. F. (2001). The psychologic distress in patients with cancer.
Nursing Clinics of North America, 36(4), 809-825.
Sobel, R. M., & Markov, D. (2005). The impact of anxiety and mood disorders on
physical disease: The worried not-so-well. Current Psychiatry Reports, 2005(7),
206-212.
Somerset, W., Stout, S. C., Miller, A. H., & Musselman, D. (2004). Breast cancer and
depression. Oncology, 18(8), 1021-1048.
Spiegel, D. (1997). Psychosocial aspects of breast cancer treatment. Seminars in
Oncology 1(Suppl. 1), S36-S47.
Spinetta, J. J. (1984). Methodology in behavioral and psychosocial cancer research.
Development of psychometric assessment methods by life cycle stages. Cancer,
15(53(Suppl. 10)), 2222-2227.
Stokes, P. E. (1993). Fluoxetine: A five year review. Clinical Therapeutics, 15(2), 216-
243.
Strauss, A. L., & Corbin, J. (1998). Basics of qualitative research: Techniques and
prodecures for developing grounded theory. Thousand Oaks, CA: Sage.
Strecher, V. J., McEvoy DeVellis, B., Becker, M. H., & Rosenstock, I. M. (1986). The
Role of Self-Efficacy in Achieving Health Behavior Change. Health Education &
Behavior, 13(1), 73-92.
Strong, V., Sharpe, M., Cull, A., Maguire, P., House, A., & Ramirez, A. (2004). Can
oncology nurses treat depression? A pilot project. Issues and Innovations in
Nursing Practice, 46(5), 542-548.
Sweeney, C., Edwards, S. L., Baumgartner, K. B., Herrick, J. S., Palmer, L. E.,
Murtaugh, M. A., et al. (2007). Recruiting Hispanic women for a population-based
study: Validity of surname search and characteristics of nonparticipants.
American Journal of Epidemiology, 166, 1210-1219.
Tableman, M. (1987). A simple way to construct a two-sample sequential confidence
interval. Biometrika, 74(3), 625-630.
175
Thase, M. E., & Ninan, P. T. (2002). New goals in the treatment of depression: Moving
toward recovery. Psychopharmacology Bulletin, 36(Suppl. 2), 24-35.
Thase, M. E., Rush, A. J., Howland, R. H., Kornstein, S. G., Kocsis, J. H., Gelenberg, A.
J., et al. (2002). Double-blind switch study of Imipramine or Sertraline treatment
of antidepressant-resistant chronic depression. Archives of General Psychiatry,
59, 233-239.
Thompson, J., Ranking, H., & Aschcroft, C. W. e. a. (1982). The treatment of depression
in general practice. Psychological Medicine, 12, 741.
Thormahlen, B., Weinryb, R.M., Noren, K., Vinnars, B., Bagedahl-Strindlund, M., &
Barber, J.P. (2003). Patient factors predicting dropout from supportive-expressive
psychotherapy for patients with personality disorders. Psychotherapy Research,
13(4), 493-509.
Tutty, S., Ludman, E. J., & Simon, G. (2005). Feasibility and acceptability of a telephone
psychotherapy program for depressed adults treated in primary care. General
Hospital Psychiatry, 27, 400-410.
Van Duyn, M. A. S., McCrae, T., Wingrove, B. K., Henderson, K. M., Boyd, J. K.,
Kagawa-Singer, M., et al. (2007). Adapting evidence-based strategies to
increase physical activity among African Americans, Hispanics, Hmong, and
Native Hawaiians: A social marketing approach. Preventing Chronic Disease
4(4), 1-11.
Van Heeringen, K., & Zivkov, M. (1996). Pharmacological treatment of depression in
cancer patients: A placebo-controlled study of Mianserin. British Journal of
Psychiatry, 169, 440-443.
Vega, W. A., Karno, M., Alegria, M., Alvidrez, J., Bernal, G., Escamilla, M., et al. (2007).
Research issues for improving treatment of U.S. Hispanics with persistent mental
disorders. Psychiatric Services, 58(3), 385-394.
Wagner, A. W., Abystrisky, A., Russo, J. E., Craske, M. G., Sherbourne, C. D., Stein, M.
B., et al. (2005). Beliefs about psychotropic medication and psychotherapy
among primary care patients with anxiety disorders. Depression and Anxiety, 21,
99-105.
Walker, L., Heys, S., Walker, M., Ogston, K., Miller, I., Hutcheon, A., et al. (1999).
Psychological status and compliance in a sample of patients treated for cancer of
the head and neck. Head Neck, 18, 269-276.
Weber, B. A., Roberts, B. L., Resnick, M., Deimling, G., Zauszniewski, J. A., Musil, C., et
al. (2004). The effect of dyadic intervention on self-efficacy, social support, and
depression for men with prostate cancer. Psycho-Oncology, 13, 47-60.
176
Wedding, U., Koch, A., Röhrig, B., Pientka, L., Sauer, H., Höffken, K., et al. (2008).
Depression and functional impairment independently contribute to decreased
quality of life in cancer patients prior to chemotherapy. Acta Oncologica, 47, 56-
62.
Weinstein, N. D. (1988). The precaution adoption process. Health Psychology, 7(4), 355-
386.
Wells, A., & Zebrack, B. (2008). Psychosocial barriers contributing to the under-
representation of racial/ethnic minorities in cancer clinical trials. Social Work in
Health Care, 46(2), 1-14.
Wells, K., Katon, W., Rogers, B., & Camp, P. (1994). Use of tranquilizers and
antidepressant medications by depressed outpatients: Results from the medical
outcomes study. American Journal of Psychiatry, 151(5), 694-700.
Wells, N. L., & Turney, M. E. (2001). Common issues facing adults with cancer. In M. M.
Lauria, E. J. Clark, J. F. Hermann & N. M. Stearns (Eds.), Social work in
oncology (pp. 27-44). Atlanta, GA: American Cancer Society.
Williams, D. G., Best, J. A., Taylor, D. W., Gilbert, J. R., & al, e. (1990). A sytematic
approach for using qualitative methods in primary prevention research. Medical
Anthropology Quarterly, 4, 391-409.
Williams, S., & Dale, J. (2006). The effectiveness of treatment for depression/depressive
symptoms in adults with cancer: A systematic review. British Journal of Cancer,
94(3), 372-390.
Winzelberg, A. J., Classen, C., Alpers, G.W., Roberts, H., Koopman, C., Adams, R.E.,
Ernst, H., Dev, P., & Barr Taylor, C. (2003). Evaluation of an internet support
group for women with primary breast cancer. Cancer, 97(5), 1164-1173.
Yancey, A. K., Ortega, A. N., & Kumanyika, S. K. (2006). Effective recruitment and
retention of minority research participants. Annual Review of Public Health, 27, 1-
28.
Young, A. S., Klapp, R., Sherbourne, C. D., & Wells, K. B. (2001). The quality of care for
depressive and anxiety disorders in the United States. Archives of General
Psychiatry, 58, 55-61.
Young, T., & Maher, J. (1999). Collecting quality of life data in EORTC clinical trials-
What happens in practice? Psycho-Oncology, 8, 260-263.
Zayas, L. H., Mckee, D., & Jankowski, K. R. B. (2004). Adapting psychosocial
intervention research to urban primary care environments: A case example.
Annals of Family Medicine, 2, 504-508.
177
Appendix 1. Telephone Interview Debriefing/ Feedback Form - English
PARENT STUDY: Alleviating Depression among Patients with Cancer (ADAPt-C)
TITLE OF PROJECT: Low-income minority cancer patients who drop out of depression
treatment
PRINCIPAL INVESTIGATOR:
Anjanette Wells, LCSW
Participant ID #______ M/F___ Age_____
Race/ethnicity_________ Occupation______________
Date of Interview______________
Time begun_____ Time ended______
Questions 1 through 4 should be filled out soon after the initial interview
1. Indicate the patient’s mood during the telephone interview.
2. Recommendations for follow-up interview.
3. Provide a brief summary of interview.
4. Interviewer critique.
Member checking - After interviewer receives completed transcription, review for
accuracy, call patient for follow-up interview, and answer questions 5 and 6. With the
patient, review the key ideas of each section for accuracy, making note of line numbers
where corrections need to be made on the transcript. (Should take about 15- 20
minutes).
5. ASK PATIENT: You have indicated that …………………………………..
a. Is this correct?
6. Is there something else you would like to add?
178
Appendix 2. Telephone Interview Debriefing/ Feedback Form - Spanish
PARENT STUDY: Alleviating Depression among Patients with Cancer (ADAPt-C)
TITLE OF PROJECT: Low-income minority cancer patients who drop out of depression
treatment
PRINCIPAL INVESTIGATOR:
Anjanette Wells, LCSW
Participant ID #______ M/F___ Age_____
Race/ethnicity_________ Occupation______________
Date of Interview______________
Time begun_____ Time ended______
Questions 1 through 4 should be filled out soon after the initial interview
1. Indicate the patient’s mood during the telephone interview.
2. Recommendations for follow-up interview.
3. Provide a brief summary of interview.
4. Interviewer critique.
Member checking - After interviewer receives completed transcription, review for
accuracy, call patient for follow-up interview, and answer questions 5 and 6. With the
patient, review the key ideas of each section for accuracy, making note of line numbers
where corrections need to be made on the transcript. (Should take about 15- 20
minutes).
5. ASK PATIENT: Usted ha indicado que …………………………………..
a. Esto es corecto?
6. Hay algo mas que quisiera decir o agregar?
Provider Name:
Provider Title:
Telephone or Face-to-face Interview:
179
Appendix 3. Provider Interview Protocol
Parent Study: Alleviating Depression among Patients with Cancer (ADAPt-C)
Study Title: Low-income minority cancer patients who drop out of depression treatment
I. Opening
a. Introduction
i. Thank you for participating today. As you know, my name is
Anjanette Wells and I am a licensed clinical social worker and
doctoral student at the USC School of Social Work. My research
interests are centered on improving retention to depression
treatment for patients with medical illness, particularly those with
cancer.
b. Purpose of Interview:
i. At some point in the recent past, you worked on the ADAPt-C
cancer and depression study or a related depression study under
Kathy Ell, DSW. As providers and project staff, I believe you have
important opinions to share about how providers might be best
able to help participants remain in depression treatment within a
depression treatment trial and not dropout. I have already began
to obtain information ADAPt-C patients on their perceptions of
depression, their perceived utility of depression treatment, barriers
to completing treatment, and their reasons for dropping out of
treatment.
Now I would like to hear from you and discuss some of your
experiences and reasons patients drop out of depression
treatment, which is important because many times provider
judgment is used as a means of defining drop-out, which may be
different than that of your patient. I want to especially have a
discussion about concrete and feasible strategies that you use or
you think would be useful in keeping your low-income cancer
patients in depression treatment.
c. Since your opinions are valuable, I will be tape-recording our
discussion today so that I do not miss any of the information that
you share with me.
d. And as an offer of appreciation for your time and input, I will either
mail or personally give you a $10 Starbucks gift card.
II. Dropout Factors/Reasons
Probes:
Are there any reasons which seem to be the most important
for patient dropout? What are they?
Provider Name:
Provider Title:
Telephone or Face-to-face Interview:
180
Are any of these reasons related to patient depression severity
or cancer symptoms or treatment? Which ones?
In what ways is patient participation in depression treatment
related to their preference for medication or counseling?
What are some early characteristics of those patients who
follow through and successfully complete treatment?
Do you notice a difference based on ethnicity?
Do you notice a difference based on gender?
III. Patient - Provider Interpersonal Factors
Probes:
Therapeutic Alliance
How important is establishing a therapeutic
alliance in retaining your patients to treatment?
Patient Motivation
Do you feel a patient’s motivation plays any role
in their follow through with depression
treatment? In what way?
As a depression care provider, what types of
things motivate or discourage you with
providing depression treatment to your
patients?
Patient Satisfaction
In what ways do patient satisfaction influence
patient dropout from depression treatment?
In what ways do you think your patients who
dropped out of treatment were dissatisfied with
their treatment plan?
III. Feasible Retention Strategies
Probe:
Is there anything that you do that seems to encourage patients
to remain in treatment? What kinds of things?
IV. Implementation challenges/ Barriers to retaining patients
Probes:
Is there anything that you do that might make it difficult to keep
your patients in treatment? What kinds of things?
What barriers do you face within the bureaucracy of the organization
of LAC + USC Medical Center system, which make it difficult for you
to try to retain your patients to treatment?
Do you get any pressure from other clinical team members to retain
your patients? What kinds of pressure?
Provider Name:
Provider Title:
Telephone or Face-to-face Interview:
181
V. Closing
As a member of this depression treatment team, do you have any
additional information about depression treatment and strategies that
you would like to share with us?
Thank you for your time today. This discussion has been extremely
beneficial.
Abstract (if available)
Abstract
Depression is one of the most common symptoms of cancer, having a profound impact on patients' quality of life, immune response, morbidity, adherence to treatment, and even mortality. Although medication and counseling are effective in reducing depressive symptoms in cancer patients, there is an increasing need to understand factors that contribute to dropout (and retention) of low-income, minority cancer patients to depression treatment.
Linked assets
University of Southern California Dissertations and Theses
Conceptually similar
PDF
Depression severity, self-care behaviors, and self-reported diabetes symptoms and daily functioning among low-income patients receiving depression care
PDF
Getting to end-of-life discussions in advanced cancer care: barriers and attitudes that limit end-of-life communication for disadvantaged Latinos
PDF
Investigating racial and ethnic disparities in patient experiences with care and health services use following colorectal cancer diagnosis among older adults with comorbid chronic conditions
PDF
Using a human factors engineering perspective to design and evaluate communication and information technology tools to support depression care and physical activity behavior change among low-inco...
Asset Metadata
Creator
Wells, Anjanette A.
(author)
Core Title
Low-income, minority cancer patients who drop out of depression treatment
School
School of Social Work
Degree
Doctor of Philosophy
Degree Program
Social Work
Publication Date
05/13/2009
Defense Date
02/23/2009
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
adherence,cancer,Depression,disparities,low-income,minority,OAI-PMH Harvest
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Palinkas, Lawrence A. (
committee chair
), Cruz, Tess B. (
committee member
), Ell, Kathleen R. (
committee member
)
Creator Email
aawells@usc.edu,awells@gwbmail.wustl.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-m2253
Unique identifier
UC1234568
Identifier
etd-Wells-2693 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-246300 (legacy record id),usctheses-m2253 (legacy record id)
Legacy Identifier
etd-Wells-2693.pdf
Dmrecord
246300
Document Type
Dissertation
Rights
Wells, Anjanette A.
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Repository Name
Libraries, University of Southern California
Repository Location
Los Angeles, California
Repository Email
cisadmin@lib.usc.edu
Tags
adherence
disparities
low-income