EXAMINING THE BURDEN OF TREATMENT WITHIN CANCER PATIENTS  

WITH MULTIMORBID CONDITIONS 

 
By 
 

Eric Andrew Vachon 

 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

A DISSERTATION 

Submitted to 

Michigan State University 

in partial fulfillment of the requirements 

for the degree of 

 

 

Nursing – Doctor of Philosophy 

2018 

 
 

 

EXAMINING THE BURDEN OF TREATMENT WITHIN CANCER PATIENTS  

WITH MULTIMORBID CONDITIONS 

ABSTRACT 

 

 

 
By 
 

Eric Andrew Vachon 

The purpose of this dissertation was to examine the concept of burden of treatment 

(BOT), from both a conceptual and operational perspective, within cancer patients with 

multimorbid conditions. BOT is the combination of a patient’s workload to manage their 

conditions, and the patient’s perspective of that workload and their conditions. Manuscript one 

was a conceptual analysis and development of a conceptual model focused on the BOT of 

cancer patients with multimorbid conditions.  Manuscripts two and three were secondary data 

analyses that utilized data from a parent trial study. The parent trial was a multi-site, randomized 

controlled trial (RCT) that tested an adherence and symptom management intervention in 

cancer patients that were newly prescribed oral oncolytic agents (OOA) over 12 weeks.  

Manuscript two examined patients’ OOA acquisition, defined as the number of days from 

the time of initial prescription until patients received their drug. This manuscript also examined 

how baseline disease/treatment factors (OOA drug class) and healthcare system factors 

(insurance type, recruitment site, and OOA copay) might predict the time to acquisition. All 272 

patients form the parent trial were included in manuscript two. The number of days to acquisition 

was collected from the patient during the baseline telephone interview. The sample was evenly 

split between males and females, had a mean age of 61 years (SD=12.2), and was primarily 

Caucasian (89%). Patients waited on average 9.73 days from the time of initial prescription to 

receive their OOAs (range 0-135 days). ANOVA results showed that those that had a copay 

waited longer to receive their OOA (P = .02). Additionally, there was a significant interaction 

effect between OOA drug class, insurance type, and OOA copay (P = .01). Simple interaction 

effects showed significant acquisition times for those prescribed kinase inhibitors, F (1, 114) = 

 

6.709, p .01, and sex hormone inhibitors, F (1, 19) = 7.879, p .01, depending on the type of 

insurance and whether or not individuals had a copay.  

Manuscript three operationally tested the conceptual model developed in manuscript 

one. This chapter examined the direct relationship between baseline antecedent characteristics 

and temporary stoppages of patients’ OOA regimens. Additionally, BOT-indicator variables were 

examined for a moderation effect on this direct relationship. OOA regimen complexity was 

utilized for the patient workload component of BOT, while patients’ rating of their symptom 

interference on daily activities was used for patient perspective. More than 36% of patients in 

the parent trial experienced a temporary stoppage of their OOA regimen over 12 weeks. The 

moderation, interaction terms between BOT and multimorbidity were statistically non-significant. 

However, females (P = .02) and those prescribed kinase inhibitors (P < .01) were more likely to 

experience temporary stoppages when compared with males and other OOA drug classes, 

respectively.    

Burden of treatment is a recently developed concept that will be valuable to research 

and practice as the prevalence of individuals with cancer and multimorbid conditions continues 

to increase. An individual’s BOT can be related to the tasks required of them to manage their 

conditions, as well as the perspective they have about these tasks and conditions. This 

dissertation provided conceptual insight into the BOT experienced by cancer patients with 

multimorbid conditions, as well as the negative outcomes they may experience if their burden 

becomes too great. Patients may experience long wait times to receive cancer treatment that is 

vital, given their critical disease status. Although non-significant, there was a descriptive trend of 

more multimorbid conditions being associated with a greater proportion of temporary stoppages, 

as well as sex and drug class having an impact on these treatment modifications. The current 

literature does not adequately describe the burden of treatment experienced by cancer patients. 

Future research and practice guidelines are needed to help identify and ease the burden of 

treatment that individuals with cancer and multimorbid conditions experience.     

 

 

This work is dedicated to my parents, Steve and Lois Vachon, for their continued support  

in all endeavors that I have undertaken throughout my life. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 

 iv 

ACKNOWLEDGEMENTS 

 
 
 
 

 

All that I have accomplished over these last three years would not be possible without 

the contributions and support of my family, friends, and the faculty and staff at Michigan State 

University. First, I would like to acknowledge my parents, Steve and Lois, who have encouraged 

and supported me through every decision and goal that I set out to accomplish. I owe so much 

to you for the person that I have become today. To my girlfriend, Lisa, who has been with me for 

the majority of this program. During times of happiness and difficulty, you were always there to 

support me through this journey.  

 

I would like to acknowledge a number of funding sources that made my doctoral 

education and dissertation work possible, including: the Michigan State University Graduate 

School for Doctoral and Dissertation Completion Fellowships; the College of Nursing for 

Scholarships and Summer Fellowships; the Oncology Nursing Society Foundation Doctoral 

Research Scholarship; and Drs. Barbara and Bill Given and Dr. Alla Sikorskii for allowing me to 

work on their grant funded by the National Cancer Institute, Improving Adherence to Oral 

Cancer Agents and Self-Care of Symptoms Using an IVR (1R01CA162401-O1A1), which 

provided an invaluable experience in helping to develop my knowledge and skills as a 

researcher. I would also like to acknowledge the American Cancer Society for awarding me with 

the Doctoral Scholarship in Cancer Nursing Grant, which funded my dissertation and allowed 

me to complete this work.  

 

 My progression as a researcher and scholar would not be possible without the guidance 

and support of my committee members. Dr. Barbara Given, who served as the chair for both my 

guidance and dissertation committees, is the gold standard by which a nurse scientist is 

measured by today. She provided me with so many opportunities over the last three years to 

grow as a scientist. She believed in me and pushed me to strive be the best that I can be. It has 

 v 

been an honor to be mentored by her. I am truly grateful for everything that Dr. Given has done 

for me. Dr. Bill Given has been an integral part of my growth as a nursing scholar. He 

challenged me to think about my work from different perspectives, and as a result, got the best 

out of me. Most importantly, Dr. Given treated me like a colleague and valued my work, which 

meant a great deal to me through this journey. Dr. Susan Dunn was a mentor to me as BSN 

student at Hope College and served on my guidance and dissertation committees. Without her 

mentorship early on in my nursing education and career, I would not have become a nurse 

scientist. I am so thankful that she encouraged me to take on new challenges and opportunities. 

She has been instrumental in my progression as a registered nurse and a nursing scholar. Dr. 

Deborah Kashy served on my dissertation committee and provided statistical consultation for 

this work. Her expertise and input were invaluable in making this dissertation a work to be proud 

of. Thank you to Dr. Denise Hershey for serving on my guidance committee and helping to 

evolve my early ideas, especially for her expertise in working with individuals with multimorbid 

conditions.  

Additionally, I would like thank Dr. Alla Sikorskii for allowing me to work alongside you for the 

last three years. The knowledge and experience that I gained working with her on the Oral 

Agents study have been vital to developing my path as a nurse scientist. Thank you to Dr. 

Rebecca Lehto who taught me how to think in a way I did not think was possible. I owe my 

conceptual works and ideas to your teaching and guidance. Thank you to Dorothy Luckie for 

always being a vital resource no matter what I needed as an employee or a student. Thank you 

to Tara Miller and Kelly Bourne for being making the process of grant writing and other tasks a 

bit less stressful, and for always being there to talk through anything while on campus. And to 

the rest of the faculty and staff in the College of Nursing, thank you for making these last three 

years an unforgettable experience. 

 

Finally, I want to acknowledge my fellow doctoral students. Completing a doctoral 

program is a rewarding and trying experience that only those that have endured it would 

 vi 

understand. Thank you all for being a helping hand or an ear to listen and making this 

experience special. Namely, Megan Flanigan for being there through every step of the journey, 

for providing motivation and encouragement along the way. To Kendra Kamp for providing a 

fresh perspective and one to listen when things got tough. Thank you to Victoria Marshall for 

allowing me to work with her and for being a guidance voice, no matter the task at hand.  

 

This has been tremendous journey that has paved a career path for me but has also 

helped me grow as a person. I wanted to thank every person that has helped me reach this 

milestone.  

 

Dissertation Funding 

Dissertation Secondary Data Analysis 

Exploring Burden of Treatment in Cancer Patients with Co-Morbid Conditions. (7/1/2017-
6/30/2019). Funded by American Cancer Society, Doctoral of Nursing Research Scholarship. 
Vachon E., Principal Investigator. Budget: $30,000. 
 
Parent Trial  

Improving Adherence to Oral Cancer Agents and Self Care of Symptoms Using an IVR. (2013- 
2017). Funded by National Institute of Health. Given, B.A., Principal Investigator, Given, C.W., 
Principal Investigator.  Budget: $2,174,769 
 

 

 

 vii 

TABLE OF CONTENTS 

 
 
 
 

LIST OF TABLES ...................................................................................................................... xi 
 
LIST OF FIGURES ....................................................................................................................xii 

CHAPTER ONE: INTRODUCTION ............................................................................................. 1 
Introduction ..................................................................................................................... 1 
Background and Significance .......................................................................................... 2 
Cancer....................................................................................................................... 2 
Oral Oncolytics .......................................................................................................... 2 
Multimorbidity ............................................................................................................ 5 
Burden of Treatment .................................................................................................. 6 
Conceptual Model ........................................................................................................... 8 
Attributes ................................................................................................................... 9 
Patient Workload ................................................................................................. 9 
Patient Perspective ............................................................................................ 10 
Antecedents ............................................................................................................ 11 
Patient Characteristics ....................................................................................... 11 
Social Factors .................................................................................................... 12 
Disease/Treatment Factors ................................................................................ 12 
Healthcare System Factors ................................................................................ 13 
Summary ................................................................................................................. 14 
Purpose......................................................................................................................... 14 
Dissertation Format ....................................................................................................... 15 
REFERENCES ................................................................................................................... 19 

 
CHAPTER TWO: BURDEN OF TREATMENT IN PATIENTS WITH CANCER AND 

MULTIMORBID CONDITIONS: A CONCEPT ANALYSIS (Manuscript One) ....................... 27 
Introduction ................................................................................................................... 27 
Purpose......................................................................................................................... 30 
Methods ........................................................................................................................ 31 
Components of Concept Analysis ............................................................................ 34 
Results .......................................................................................................................... 35 
Attributes ................................................................................................................. 35 
Patient Workload ............................................................................................... 35 
Patient Perspective ............................................................................................ 37 
Antecedents ............................................................................................................ 38 
Patient Characteristics ....................................................................................... 38 
Social Factors .................................................................................................... 38 
Disease/Treatment Factors ................................................................................ 39 
Healthcare System Factors ................................................................................ 39 
Consequences ........................................................................................................ 40 
Adherence/Non-adherence ................................................................................ 40 
Clinical Outcomes .............................................................................................. 40 
Treatment Modifications..................................................................................... 41 
Health and Well-being ....................................................................................... 42 
Burden of Treatment Morbidity (BOTM) Model ........................................................ 42 

 viii 

Discussion ..................................................................................................................... 43 
Limitations ..................................................................................................................... 46 
Conclusions .................................................................................................................. 47 
REFERNCES ...................................................................................................................... 50 
 

CHAPTER THREE: HEALTHCARE SYSTEM CHALLENGES IN PATIENT ACQUISITION OF 

ORAL ONCOLYTIC AGENTS (Manuscript Two) ................................................................. 56 
Introduction ................................................................................................................... 56 
Purpose......................................................................................................................... 58 
Conceptual Model ......................................................................................................... 58 
Methods ........................................................................................................................ 59 
Sample .................................................................................................................... 60 
Parent Study Sample ......................................................................................... 60 
Data Collection .................................................................................................. 60 
Secondary Data Sample .................................................................................... 61 
Measures ................................................................................................................ 61 
Data Analysis .......................................................................................................... 62 
Results .......................................................................................................................... 63 
Discussion ..................................................................................................................... 64 
Limitations ..................................................................................................................... 67 
Conclusions .................................................................................................................. 67 
APPENDICES ..................................................................................................................... 74 
APPENDIX A: FDA Approved Oral Oncolytic Agents Included in the Parent Study ....... 75 
APPENDIX B: Eastern Cooperative Oncology Group Scale of Performance Status & 

Karnofsky Status Performance Scale ..................................................... 76 
APPENDIX C: Parent Study CONSORT Flow Chart ..................................................... 77 
APPENDIX D: Parent Study Medical Record Audit........................................................ 78 
APPENDIX E: Parent Study Screening/Baseline Data Collection Tools ........................ 90 
REFERENCES ................................................................................................................... 93 
 

CHAPTER FOUR: THE IMPACT OF BURDEN OF TREATMENT ON TEMPORARY 
STOPPAGES OF TREATMENT IN CANCER PATIENTS WITH MULTIMORBID 
CONDITIONS (Manuscript Three)....................................................................................... 96 
Introduction ................................................................................................................... 96 
Purpose....................................................................................................................... 100 
Conceptual Model ....................................................................................................... 100 
Methods ...................................................................................................................... 101 
Sample .................................................................................................................. 101 
Parent Study Sample ....................................................................................... 101 
Data Collection ................................................................................................ 102 
Secondary Data Sample .................................................................................. 102 
Measures .............................................................................................................. 103 
Data Analysis ........................................................................................................ 107 
Results ........................................................................................................................ 108 
Discussion ................................................................................................................... 110 
Limitations ................................................................................................................... 113 
Conclusions ................................................................................................................ 113 
APPENDICES ................................................................................................................... 117 
APPENDIX A: FDA Approved Oral Oncolytic Agents Included in the Parent Study ..... 118 
APPENDIX B: Eastern Cooperative Oncology Group Scale of Performance Status & 

Karnofsky Status Performance Scale ………………………………..……120 

 ix 

APPENDIX C: Parent Study CONSORT Flow Chart ................................................... 120 
APPENDIX D: Parent Study Medical Record Audit...................................................... 121 
APPENDIX E: Parent Study Screening/Baseline Data Collection Tools ...................... 133 
APPENDIX F: Cancer Symptom Experience Inventory ............................................... 136 
REFERENCES ................................................................................................................. 142 

 
CHAPTER FIVE: CONCLUSIONS .......................................................................................... 148 
Introduction ................................................................................................................. 148 
Overview of Manuscript One ....................................................................................... 150 
Overview of Manuscript Two ....................................................................................... 152 
Overview of Manuscript Three ..................................................................................... 153 
Limitations ................................................................................................................... 154 
Implications ................................................................................................................. 155 
Nursing Research .................................................................................................. 155 
Nursing Practice .................................................................................................... 159 
Policy..................................................................................................................... 160 
Conclusions ................................................................................................................ 161 
REFERENCES ................................................................................................................. 163 

 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 x 

 

 
 

Table 2.1. Rodger’s Evolutionary Concept Analysis Process .................................................... 32 
 
Table 2.2. Study Details for Included Articles (n=29) ................................................................. 48 
 
Table 3.1. Secondary Data Analysis OOA Acquisition Variables ............................................... 62 
 
Table 3.2. Descriptive Statistics for the Secondary Data Analysis Study Sample (N=272) ........ 70 
 
Table 3.3. Unadjusted and Adjusted Mean Days to Receipt of OOAs by Healthcare System 

Factors .................................................................................................................... 71 

 
Table 3.4. ANOVA Results for Effect of Healthcare System Factors on Days to Receipt of OOAs
 ................................................................................................................................ 71 

 
Table 3.5. FDA Approved Oral Oncolytic Agents Included in the Parent Study ......................... 75 
 
Table 3.6. Eastern Cooperative Oncology Group Scale of Performance Status & Karnofsky 

Status Performance Scale ....................................................................................... 76 

 
Table 4.1. Secondary Data Analysis BOT Moderation Variables ............................................. 103 
 
Table 4.2. Medication Regimen Complexity Index (MRCI) Adaptation Description .................. 105 
 
Table 4.3. Descriptive Statistics for the Secondary Data Analysis Study Sample (N=272) ...... 114 
 
Table 4.4. Sex Differences of Predictor Variables (N=272) ..................................................... 114 
 
Table 4.5. Correlations of Continuous Predictor Variables ...................................................... 115 
 
Table 4.6. Frequency of Temporary Interruptions by Baseline Antecedent Factors (N=272) ... 115 
 
Table 4.7. Multimorbidity x Regimen Complexity Moderation Effect on Temporary Stoppages 116 
 
Table 4.8. Multimorbidity x Symptom Interference Moderation Effect on Temporary      

Stoppages ............................................................................................................. 116 

 
Table 4.9. Predictor Variable Main Effects on Temporary Stoppages ..................................... 116 
 
Table 4.10. FDA Approved Oral Oncolytic Agents Included in the Parent Study ..................... 118 
 
Table 4.11. Eastern Cooperative Oncology Group Scale of Performance Status & Karnofsky 

Status Performance Scale.................................................................................... 119 

 

LIST OF TABLES 

 
 
 

 

 

 xi 

LIST OF FIGURES 

 
 

 
 
Figure 1.1. Burden of Treatment Morbidity (BOTM) Model .......................................................... 9 
 
Figure 2.1. PRISMA Flow Diagram of Reviewed Articles .......................................................... 34 
 
Figure 2.2. Burden of Treatment Morbidity (BOTM) Model ........................................................ 43 
 
Figure 3.1. Burden of Treatment Acquisition Analysis Model .................................................... 59 
 
Figure 3.2. ANOVA Simple Interaction Effects Plots on OOA Drug Class ................................. 72 
 
Figure 3.3. Parent Study CONSORT Flow Chart ...................................................................... 77 
 
Figure 4.1. Burden of Treatment Moderation Analysis Model   ................................................ 101 
 
Figure 4.2. Parent Study CONSORT Flow Chart .................................................................... 120 
 
 
 
 
 
 
 
 
 
 

 

 xii 

Introduction 

CHAPTER ONE: INTRODUCTION 

The Centers for Disease Control and Prevention (CDC) estimates that one in four US adults 

have at least one chronic condition; this prevalence increases to three in four adults over the 

age of 65 years.1 As an individual ages, they are at risk of having more than one chronic 

condition, which is known as multimorbidity. Cancer is one of the top 10 most prevalent chronic 

diseases, as well as the second leading cause of death among adults in the US.1,2 As advances 

in cancer treatment continue, individuals are likely to live longer, and are, conversely, at risk of 

developing other chronic diseases or developing cancer while already having another chronic 

condition.3 The presence of both cancer and another chronic disease(s) presents unique 

challenges for patients, caregivers/families, and healthcare providers. The required tasks for 

these patients to manage their diseases are known as the burden of treatment.4-7 Burden of 

treatment (BOT) is comprised of two primary components: 1) the workload of being a patient 

(i.e., the healthcare tasks required to manage one’s condition(s)) and 2) the patient’s 

perspective of that workload.5 The components of BOT are necessary, in order to understand 

the patient experience when trying to manage multiple morbidities. Both the patient workload 

and patient perspective may have an impact on the patient and clinical outcomes, including 

quality of life, utilization of healthcare resources, and continuation of treatment.4-9 

Burden of treatment is a developing concept that has yet to be examined within cancer 

patients with multimorbid conditions.10-12 As cancer patients continue to live longer, and have a 

greater risk for other chronic conditions, research will be vital to examine how to empower this 

population to effectively self-manage their conditions as well as how to decrease adverse 

clinical events, such as hospitalizations. This research will focus on the BOT experienced by 

cancer patients who have been prescribed oral oncolytic agents (OOA) who also have to 

manage other chronic conditions in terms of four categories: patient factors, social factors, 

healthcare system factors, and disease/treatment-related factors. Three separate but 

 

 
1 

interrelated manuscripts were completed in order to examine burden of treatment within this 

population. This dissertation includes a conceptual manuscript and two data-driven manuscripts 

utilizing a secondary analysis of a parent, clinical trial study. 

Background and Significance 

Cancer 

According to the American Cancer Society (ACS), more than 1.7 million individuals will be newly 

diagnosed with cancer in 2018, as well as an estimated 15.5 million individuals are living with 

cancer or have a personal history of cancer.13 Although these numbers are staggering, cancer 

mortality has greatly decreased in the last several years. From the height of cancer mortality in 

1991 to 2015, the mortality rate has since decreased by more than 26%.13 Improvements to life 

expectancy within cancer patients are due in part to decreased cancer risk factors, such as 

decreased smoking rates, better detection methods, and advances in available treatment.3,14 

Treatment advances, such as OOAs, have allowed oncology providers to better target cancer 

cells and specific genetic mutations.15,16 The goal of newly-developed treatments is to increase 

length of survival and quality of life within cancer patients. As these survival rates continue to 

improve, there will be a greater need for the development of interventions to help cancer 

patients manage the workload brought on by their complex cancer treatment. With improved 

cancer survival rates, and longer life expectancies in the overall US population, the combination 

of cancer and other chronic diseases can bring about a more diverse array of healthcare tasks 

for patients and healthcare providers to manage 

Oral Oncolytics 

One form of cancer treatment being prescribed more frequently are OOAs. Halfdanarson and 

Jatoi indicated that in 2010, 10% of all chemotherapy medications prescribed were oral 

agents.17 Bassan et al. reported in 2014 that 20–25% of all chemotherapy treatment was 

administered orally.18 These statistics indicate oral oncolytic usage increased 15% in only four 

 

 
2 

years. This upward trend of OOA use will likely continue due to the large number of oral 

chemotherapy medications awaiting FDA approval.19,20  

With recent treatment advances, such as OOAs, cancer has become a chronic disease 

requiring life-long treatment.21,22 This paradigm shift in cancer treatment—from intravenous (IV) 

chemotherapy to oral agents—presents new obstacles for providers and patients. While IV 

chemotherapy administration is managed in a controlled environment by trained healthcare 

professionals, oral agents lack such firsthand monitoring, thereby shifting treatment 

responsibility to patients and their families, thus increasing the patient workload.20 These oral 

regimens can be quite complex, being comprised of multiple oral medications with varying 

dosages, cycles (days on and days off), and special instructions (e.g., avoid certain medications 

or food).23 These complexities are only taking into account the requirements to self-administer 

the OOAs.4-9,22 Patients are also required to manage the symptoms and side effects brought on 

by their OOA regimens, which patients may not be adequately prepared for when self-

administering cancer treatment in the home as opposed to receiving IV chemotherapy in the 

clinic.15,18,22,24 Add to this another chronic condition that requires management and the workload 

and BOT may be multiplied, which can impact the patient’s perspective of their disease and 

ability to manage multiple chronic conditions. 

With the introduction of oral oncolytics, the delivery of cancer care has changed 

significantly, with management responsibility shifted from healthcare providers to patients and 

caregivers.15,24-26 The most commonly discussed topics in the literature regarding oral oncolytics 

are medication management, symptom and side effect burden, and financial concerns.18,21,27-33 

Properly managing oral oncolytics is crucial for reducing disease progression, maintaining 

functionality and quality of life, and preventing premature death.15,16,23,25,34,35 However, several 

factors make managing oral oncolytics difficult. These factors fall into the following four 

categories: patient, social, healthcare system, and disease/treatment-related factors.23 This 

dissteration focuses on components of these four categories, but will emphasize the 

 

 
3 

disease/treatment and healthcare system factors in relation to cancer patients with multimorbid 

conditions. Having an additional chronic disease can impact the amount of work that an 

individual must complete to control their conditions. This increased workload from other 

conditions may affect an individual’s ability to manage their cancer treatment, especially oral 

oncolytics. Healthcare system factors can make it difficult for patients to adequately manage 

their cancer treatment when prescription errors, difficulties with insurance coverage, and slow 

delivery by specialty pharmacies delay patients acquiring their oral oncolytics.36-38 These factors 

contribute to the BOT and its impact on cancer patients’ treatment course.  

Due to the differences in cancer types and drug classes, the majority of the oral oncolytic 

literature does not isolate specific side effects unless the study examined a single drug. 

However, the most common side effects noted in the literature are diarrhea, nausea, skin 

complications (e.g., rash, blisters, or dry skin), oral mucositis, fatigue, sleep disturbance, and 

neuropathy. 15,19,28,33,39 Because most cancer and multimorbidity management takes place in the 

home, it is imperative that providers have open communication with patients to provide 

education and keep patients informed about possible side effects and management strategies, 

as well as to monitor how patients are doing. Unmanaged side effects may require the need for 

dose modifications, medication interruptions, a switch to IV agents, discontinuation of cancer 

treatments, or utilization of healthcare resources.40-42  

Patients may face financial difficulties due to out of pocket (OOP) costs of OOAs, which 

can total several thousand dollars per month.37,43-45 Additionally, difficulties with insurance 

coverage and the need to use specialty pharmacies can lead to patients discontinuing or 

delaying necessary cancer treatments.33,38,45,46 Whether it is having to pay for their OOAs or 

manage the symptoms and side effects, OOAs add to the workload of being a cancer patient by 

requiring more responsibility of the patient.36 The challenges accompanied by OOA regimens 

and symptom and side effect management may be further exacerbated for patients who have 

multiple chronic conditions that also require complex management. 

 

 
4 

Multimorbidity 

Multimorbidity, or the presence of multiple chronic conditions, has become more common for 

individuals than any single chronic condition in the United States.47 Researchers estimate that 

by the year 2020, 81 million people will be suffering from multimorbidity,48 a condition that can 

be difficult for healthcare providers and patients to manage. The American Association of 

Retired Persons (AARP) estimates that more than 50% of those over 65 years old with cancer 

have multiple chronic conditions.49 Multimorbidity has been associated with decreased patient 

functionality and quality of life, increased healthcare utilization, exacerbation of chronic 

conditions, and increased mortality.50 Despite its prevalence, the cancer literature has only 

recently included multimorbidity. One reason for this lack of attention is the strict eligibility 

criteria of cancer treatment drug trials. Most patients with other chronic conditions, especially 

older adults, are not eligible for these drug trial studies due in part to the possible interaction of 

the other prescription drugs with the trialed medications.3,14 Within the limited literature, 

multimorbidity in cancer patients has been associated with higher rates of complications and 

side effects, decreased quality of life, increased disability, higher healthcare spending, greater 

financial burden, and decreased survival.4,8,9,50 Due to this limited evidence, it has been difficult 

for providers to develop evidence-based care and treatment plans for cancer patients with 

multimorbidities. Treatment plans have been primarily centered around a single-disease 

approach, as opposed to focusing on both cancer and other conditions.3 

The existing cancer multimorbidity literature includes studies in which researchers 

inquire about cancer patients’ other conditions and examine correlations between the other 

chronic conditions and cancer.12,51 However, no BOT studies were identified that specifically 

target cancer patients with multimorbid conditions. The topic of cancer patients with 

multimorbid conditions taking an oral oncolytic has also not been addressed. While the 

number of individuals with cancer and other conditions is expected to increase, along with 

their use of OOAs, the pace of research with this population lags behind what it should be. 

 

 
5 

Without studies specifically targeting cancer patients with multimorbid conditions, 

understanding their BOT and developing interventions to empower patients to manage their 

diseases cannot happen. This dissertation lays the foundation for a program of research 

focused on examining the increased BOT experienced by individuals with cancer and other 

chronic conditions, with the goal of developing interventions to equip these patients to manage 

their burden of treatment more effectively.  

Burden of Treatment 

Burden of treatment is the combination of patient workload and patient perspective. Patient 

workload is the required tasks needed for patients to manage their conditions. While, patient 

perspective is the way in which patients perceive their workload and conditions. Burden of 

treatment has only been a topic of discussion within the last decade, with the continued rise of 

chronic conditions and multimorbidity.4-7,10-12,50,52-57 The development of this concept was needed 

in order to articulate the work that must be done by patients to manage their chronic 

conditions.4,6 BOT as a concept has become important due to the shift to shorter inpatient stays 

and a greater emphasis on patient self-management in the home. This emphasis on self-

management has led to patients being more responsible for their care, and therefore a greater 

workload, or burden, may be imposed upon them. Tran, Sav, and Eton are the three primary 

contributors to the development of BOT literature. Their work began with the conceptualization 

of BOT and has transitioned to developing empirical BOT measures for use within certain 

chronic disease populations.5,6,9  

Along with chronic conditions55,56 in general, and multimorbidity,50 BOT, as a concept, 

has been utilized within specific chronic conditions, including HIV/AIDS,52,54,58 mental illness,15,17 

diabetes,59 cardiovascular diseases,60 cystic fibrosis,53 hyperlipidemia,55 asthma,6,55 and 

stroke.9,55 These chronic condition studies have varied in design, predictor variables, and 

outcomes, although outcomes have been seldom analyzed as most studies have been 

exploratory. For the qualitative studies, researchers examined themes surrounding BOT within 

 

 
6 

their population of interest.4,7,55,57 These themes are discussed later in this dissertation within the 

components of burden of treatment. While some studies have discussed BOT from a conceptual 

standpoint through literature reviews,5,52,54 the quantitative studies have supplemented indicator 

variables in order to better understand components of BOT. As opposed to utilizing a BOT 

specific measure, these works included variables that acted as a substitute. Indicator variables 

were used that are components of BOT as opposed to a validated BOT-specific measure. 

These indicator variables have consisted of treatment complexity,53,55 number of 

medications,54,57 number of interactions with healthcare providers,10-12 and difficulty managing 

treatment.55-57 Due to the use of indicator variables as opposed to BOT-specific measures, it can 

be difficult for researchers and clinicians to utilize BOT literature, especially for cancer patients 

where BOT has only been a recent focus.  

Three studies were found to center around the concept of BOT within cancer patients. 

Two of the studies utilized data from national databases, Surveillance Epidemiology and End 

Results (SEER-Medicare)12 and an institutional cancer registry,10 while the other study recruited 

patients through a national survey registry and had direct contact with participants.11 These 

studies evaluated burden based on the number of days interacting with the healthcare 

system,10-12 type of interaction (e.g., receiving cancer treatment, clinic visit, or ER/acute stay),10-

12 number of physicians the patients were interacting with,12 number of medications,12 and 

symptom burden.11 Only one of the studies measured and included multimorbidities within their 

analysis.12 Presley et al. found that those with a greater number of multimorbid conditions were 

more likely to experience a higher BOT, as measured by interaction time with the healthcare 

system and number of prescription medications. Although these measures are primary 

components of BOT, these variables do not begin to tell the complete story for these cancers 

patients because they do not describe all of the healthcare activities patients needed to 

complete or convey how patients perceive the difficulties and complexities with these tasks.  

 

 
7 

The lack of research examining patients’ perspective of their workload has been the 

continued challenge for researchers undertaking BOT work with any chronic disease 

population.4,5,7 The current BOT literature within cancer patients has only utilized indicator 

variables to understand the concept of BOT. This work, being a secondary data analysis, will do 

the same, while future works in this program of research will utilize BOT-specific measures 

within cancer patients with multimorbid conditions. Although this study will utilize multiple 

indicator variables, it provides preliminary data and lays the foundation for future studies 

empirically measuring BOT, and ultimately developing strategies and interventions to combat 

the daily challenges faced by cancer patients with multimorbid conditions.  

Conceptual Model 

This dissertation is driven by a derived theoretical model called the Burden of Treatment 

Morbidity (BOTM) Model, which is essential in guiding this work and future studies within this 

program of research. The BOTM (Figure 1) is derived from the work of three primary 

contributing research teams to the BOT literature and the concept analysis that will be 

discussed in chapter 2.4-9 Tran, Sav, Eton, and their teams have laid the foundation for BOT 

literature through their conceptual framework development. Similar to Sav et al.,5 the BOTM 

follows Rodger’s evolutionary concept analysis method.61,62 The BOTM works to explain how 

BOT impacts the daily life of an individual with chronic disease(s). The model begins with the 

four antecedent categories of factors that precede BOT, which were mentioned earlier in the 

chapter (patient, social, healthcare system, disease/treatment). These factors directly impact the 

attributes of burden of treatment, as well as the consequences, much like a mediation model. 

Additionally, there are feedback loops from the attributes of BOT and the consequences back to 

the antecedents. These feedback loops illustrate the dynamic nature of BOT, as the different 

components have the potential to be continuously changing and impacting one another. The 

ensuing sections will lay out the three primary components of the BOTM (antecedents, 

 

 
8 

attributes, and consequences), as well as describe the directional relationships of the 

components. 

 

Figure 1.1. Burden of Treatment Morbidity (BOTM) Model  

 

 

Attributes 

Burden of treatment is comprised of two primary components: patient workload and patient 

perspective.  

Patient Workload. As exemplified by the standard definition of burden of treatment, “the 

workload of being a patient,” BOT is primarily viewed within the context of the required tasks 

that individuals must undertake in order to manage their chronic disease(s).4-7 These activities 

will vary depending on the disease population and the individual patient. However, the most 

commonly discussed activities break down into the following categories: learning about one’s 

disease, medication management, nonpharmacological/equipment management (e.g. CPAP 

machine), symptoms and side effects management, lifestyle changes (e.g. diet and exercise), 

 

 
9 

healthcare system interactions, and financial responsibilities.4-7,10-12,50,52-57,63 Different tasks are 

encompassed by these overarching categories and will be influenced by certain antecedents, 

such as disease or treatment factors and healthcare provider decisions. Dependent upon the 

quantity and complexity of these tasks, as well as some individual and social characteristics 

(e.g. marital status), patients may view their workload as stressful or overwhelming, and this 

stress may impact their ability to manage their disease(s) and their overall quality of life.5 The 

patient workload can change depending on the number of diseases that an individual has, as 

well as the complexity of the treatment regimens to manage these diseases. One would 

anticipate that those with more chronic diseases and subsequent treatment will have more tasks 

to complete in order to manage these diseases.4,5,7,9 

Patient Perspective. The patient perspective is how an individual perceives their disease(s) and 

the tasks needed to manage their illnesses. The three primary BOT contributors discussed the 

Normalization Process Theory (NPT) as influencing their conceptualization of BOT.4,5,7,(cite 

NPT). The NPT focused on the integration of practices into an individual’s or group’s everyday 

routine. For individuals with chronic disease, NPT applies to the need to integrate healthcare 

tasks into everyday life in order to manage their disease. One component of the NPT, “reflexive 

monitoring,” focuses on the subjective viewpoints of individuals and groups when integrating 

practices and tasks into everyday life. Reflexive monitoring specifically examines how 

individuals and groups evaluate and mentally process the impact of integrating practices into 

life. Reflexive monitoring provides an underpinning for the subjective patient perspective within 

the concept of BOT, and how individuals with chronic diseases perceive their diseases and 

associated workload.  

 

Patients may have negative reactions, such as fear, anger, and distress, which may 

negatively impact their outcomes. However, when researchers or clinicians are able assist 

patients in adapting their illnesses perceptions, patients are able to improve self-management 

and recovery from acute adverse events.68,69 As the patient workload changes, so can the 

 

 
10 

patient perspective. With the addition of a chronic condition, the patient workload changes; 

therefore, the patient perspective changes with the new paradigm. This change in perspective 

may be positive or negative, depending on the intensity of the workload change and how difficult 

it may be for the patient to adapt.5,6 The goal in measuring this component of BOT is to 

understand patients’ perspective about their illness and the associated workload to be able to 

positively alter these perceptions and empower patients to effectively self-manage their 

diseases. Before the two components of BOT can be examined, there are preceding factors that 

must take place. These preceding factors are known as antecedents.  

Antecedents 

Antecedents are the four preceding categories of factors (patient, social, healthcare system, 

disease/treatment) that must occur before the examination of BOT can begin. 

Patient Characteristics. Depending on the disease population, patient demographics can have a 

substantial impact on BOT and the associated outcomes. Age, sex, race, socioeconomic status, 

employment status, and education level can all have an impact on burden,4-6,50,54 either through 

biological processes or by having the knowledge and resources available to receive quality 

care. Patients’ functional status, whether physical or cognitive, also plays a role on how patients 

are able to adapt and manage their disease(s).70-73 A major patient characteristic that may 

impact burden is the patients’ psychological status, such as having anxiety or depression.74-79 

This is not the same as the patient perspective, but one’s psychological wellbeing can frame an 

individual’s perspective on their disease(s) and the associated burden, as well as impact their 

ability to adapt and be resilient to that burden. For example, an individual with depression or 

anxiety may have a more difficult time adapting to a higher workload, therefore increasing their 

level of distress. The patient perspective and ability to reframe the situation are truly the keys to 

the level of impact that burden of treatment may have on a patient’s health and overall quality of 

life.  

 

 
11 

Social Factors. Family and friends are often associated with helping patients through difficult 

times. Patients having increased social support can lead to a lower burden of treatment, if their 

loved ones are able to assist in managing some of the healthcare tasks.80-84 However, the sense 

of burden can also go the other way. Some patients feel an increased burden if they believe that 

they, and their disease, are having a negative impact on the lives of their loved ones.5,9 It is 

important to understand the social dynamic of individuals with chronic disease, and the impact 

the patients feel their support system (i.e. spouse and family members) has on them, but also 

their perception of how their disease(s) impact the lives of those around them.  

Disease/Treatment Factors. When working with BOT, the issues surrounding disease and 

treatment factors may be the most influential, as well as the most difficult, to control for patients. 

Especially when working with multimorbid individuals, the difficulties managing diseases with 

multiple associated treatments can be a daunting task. When there are interactions among 

diseases and/or medications, management of diseases and treatment can become even more 

difficult, even impossible, for patients to self-manage, requiring intervention from healthcare 

providers.4-6,21,50 For example, an individual with cancer and diabetes may be prescribed an oral 

oncolytic combined with a corticosteroid, which has a common side effect of elevated blood 

sugars. This cancer treatment regimen presents a new challenge for a cancer patient to 

manage, but they also need to worry about how the cancer treatment may affect their diabetes 

and their ability to manage their diabetes. In addition, those with cancer face particular 

challenges with constant changes to treatment regimens, especially dose reductions and 

temporary stoppages with OOAs.  

With the shift towards outpatient care and increased management in the home, patients 

are being tasked with a greater responsibility of self-management. However, patients may not 

have the knowledge or confidence to understand how to effectively manage the competing 

characteristics of multiple diseases and their treatments, which can impact the patient 

perspective and their overall burden. In the earlier example, the patient may not be aware that 

 

 
12 

steroids can impact their blood sugars. The patient must rely on their oncologist or nurse to 

know that they have diabetes and are being prescribed a steroid. The oncologist or nurse will 

thus be able to work with the patient to develop or modify strategies to manage their disease 

and associated treatment. This example illustrates how one added medication or the diagnosis 

of a new chronic disease can have a substantial impact on the patient and their ability to 

manage their multiple chronic conditions. The increased number of healthcare tasks, combined 

with the overwhelming responsibility, leads to an increased burden among individuals with 

cancer and other chronic conditions.4,7,10,12,50,56,57  

Healthcare System Factors. Although more responsibility is being placed on the patient to 

manage their chronic disease(s), the healthcare system plays a substantial role in patients 

being successfully able to effectively self-manage in the home.6,11,52 The healthcare system 

must do a great deal of work at the onset to educate and prepare patients before they are ready 

to take control of their disease and treatment regimen. Adequately preparing patients to self-

manage their conditions is made possible through effective education and open communication 

between providers and patients. Patients must trust their provider and feel that their voice is 

heard, or they are less likely to act on the advice from their providers.85-88 Lack of patient-

provider communication or too much being asked of patients can be stressful and lead to an 

increased burden for patients. Patients may experience increased stress when having extended 

wait times to schedule appointments with specialists, such as oncologists. Looking specifically 

at oral oncolytics, acquisition of OOAs may be made difficult due to delayed insurance 

approvals because of rising costs,37,38 increased regimen changes may bring about prescription 

errors,38 and waiting for a specialty pharmacy to fill or ship the prescription. OOAs are not as 

readily available to patients as most other medication, and it can therefore be stressful for 

patients with late-stage disease waiting to receive a critical medication.4,6,38 Patients need to be 

able to trust the healthcare system and feel their needs are being addressed, or the foundation 

of their disease management begins to break down.  

 

 
13 

Summary 

This model adds to the existing literature by displaying the interactive and dynamic relationship 

of patient workload and patient perception, the primary components of BOT. The BOTM also 

shows how the different components of the model interact with one another, either having a 

direct or indirect impact. The antecedent categories may have a direct impact on the 

consequences or may have an impact on burden of treatment, which then impacts the 

consequences. At the same time, BOT has a feedback loop with the antecedent categories, as 

does the ultimate consequences. What the BOTM shows is the chronic disease and BOT 

process is dynamic, and therefore constantly changing. When a decision is made by the patient 

or the healthcare provider, or a new medication or lifestyle change is added to the list of tasks 

needed to be completed, all aspects of the model have the potential to be impacted, especially 

the patient perspective. This model helped guide this work to better describe the BOT faced by 

cancer patients with multimorbidities. It also serves as the foundation for a future program of 

research focused on empowering this population to take control of their diseases and ease their 

burden of treatment.  

Purpose 

The purpose of this work is to examine burden of treatment from multiple viewpoints among 

cancer patients with multimorbid conditions. This three-part dissertation 1) describes BOT from 

a conceptual standpoint; 2) describes the healthcare system factors that impact patient 

acquisition of OOAs; and 3) examines how BOT may moderate the relationship between cancer 

patients’ antecedents and their continuation on their cancer treatment. These three parts are 

distinct manuscripts that share common themes throughout to create one synchronized 

dissertation. This work begins to address a number of gaps in both BOT and cancer literature. It 

examines BOT in cancer patients with multimorbid conditions conceptually, which has not been 

done for this specific population. This work also uses indicator variables for BOT, which is 

common in the current literature, but this work examines BOT’s impact on cancer patients’ 

 

 
14 

continuation of care, which has not been previously done analytically. This dissertation work 

lays a foundation for a program of research focused on developing strategies and interventions 

to empower cancer patients with multimorbid conditions to effectively self-manage their cancer 

and other disease(s).  

Dissertation Format                                                                                                      

Due to the continued emphasis placed on production and manuscript publication, this 

dissertation follows the three-manuscript approach. The dissertation is a secondary analysis of 

a parent trial study89 focused on management interventions for cancer patients newly prescribed 

OOAs. As previously mentioned, the three manuscripts are as follows: 1) a conceptual analysis 

examining the burden of treatment among cancer patients with multimorbid conditions; 2) a 

data-driven paper describing the time from prescription to patient receipt of OOAs and what 

healthcare system variables might predict this acquisition time; and 3) a moderation analysis 

examining how the burden of treatment of patients in the parent study may impact the 

relationship of baseline antecedents and consequence of patients continuing on their cancer 

treatment. 

Chapter 2 will present a concept analysis manuscript that utilizes Rodger’s evolutionary 

concept analysis process.61,62 This chapter involves an in-depth discussion of BOT and the 

Burden of Treatment Morbidity Model. The paper will be in the context of cancer patients with 

multimorbid conditions and the challenges faced by these patients, as evidenced by the 

literature. This concept analysis is not limited to those cancer patients that have been 

prescribed OOAs, but includes all cancer patients that also have other chronic disease(s) that 

require treatment. The primary focus of this chapter is on the concept of BOT within the specific 

population. This paper will plan to be submitted to the Journal of Advanced Nursing, as this 

journal is one of the primary publishers of concept analysis works in the nursing literature.  

Chapter 3 will be a secondary data analysis describing the healthcare system factors 

that have impacted patients’ acquisition of their oral oncolytic agents. The secondary analysis 

 

 
15 

was of a parent trial, which focused on management interventions in patients that were newly 

prescribed OOAs. For this work, acquisition was the time from OOA prescription to patient 

receiving the medication.90 The following variables were utilized: 

•  Antecedents: Healthcare system: insurance type (government/private), OOA copay 

(yes/no), and patient recruitment site. Disease/Treatment: OOA medication class. 

•  Consequence: time to acquisition of OOA (amount of time from initial prescription until 

the patient receives their OOA).  

Chapter 3, first, descriptively examines the time to acquisition for patients’ oral oncolytics. After 

describing the time to acquisition, analysis of variance was utilized to analyze how insurance 

type, whether patients have a copay for their OOA at initiation or not, patient recruitment site, 

and OOA medication class might predict the time to acquisition.  

Chapter 3 also includes some qualitative work within the patient interview call logs, in 

order to identify specific examples of patients experiencing an error or delay by the healthcare 

system in acquiring their OOA. Some examples may be prescriber errors, insurance delays, 

pharmacy errors, or specialty pharmacy shipment delays. These examples are included in the 

discussion to provide examples of real patient experience in acquiring their oral agents for 

clinicians and researchers to consider during clinical practice and future research. 

Chapter 4 is a secondary data analysis examining the relationship between baseline 

antecedents, BOT variables, and continuation of cancer treatment. The secondary analysis is 

from the same parent study as Chapter 3. It is hypothesized that there are relationships 

between baseline antecedents and consequences experienced by cancer patients, thus BOT 

was tested as a moderating variable of this hypothesized relationship. Although the parent study 

did not include a specific BOT measure or scale, indicator variables were used that were 

included in the parent trial study. This chapter utilizes the following variables from the parent 

trial: 

 

 
16 

•  Antecedents: patient—age, sex, and employment status (employed/not employed). 

Social: marital status (married/single). Healthcare system: insurance type 

(government/private) and OOA copay (yes/no). Disease/Treatment: number of chronic 

conditions (other than cancer) and class of OOA. 

•  Burden of treatment: cancer treatment regimen complexity, as measured by the modified 

medication regimen complexity index,91 and the level of daily interference from cancer 

symptoms at baseline. 

•  Consequences: temporary stoppages in the patient’s cancer treatment protocol will be 

utilized for continuation of care. A temporary stoppage will constitute any added delay in 

the treatment protocol, which was not part of the prescribed regimen. Due to insufficient 

counts at each time period of the parent trial study, temporary stoppages were 

measured by whether or not an individual experienced a temporary stoppage or not over 

the course of the entire trial. 

A moderation logistic regression analysis was utilized to describe how the BOT indicator 

variables moderate the relationship between the antecedents and the consequence of 

temporary stoppages of patients’ oral oncolytic regimens. All of the antecedent and BOT 

variables were baseline measures, while the temporary stoppages were over the course of the 

trial. Because the emphasis of this dissertation is focused on the added burden of treatment by 

multimorbid conditions on cancer patients, the BOT moderation variables were added into the 

model as interactions with the multimorbid conditions antecedent variable. There were two 

moderation models, because there are two separate BOT-indicator variables that are being 

considered as moderators (symptom interference and cancer treatment regimen complexity).  

Chapter 5 will summarize the findings of the three manuscripts as individual papers, as 

well as the themes that tie them together. The final chapter also discusses the gaps addressed 

by the overall work, limitations of the dissertation study, and the future studies that will be 

 

 
17 

included within this program of research and how they will contribute to the nursing and 

oncology literature. This section also discusses the implications these papers have on research, 

practice, and policy within the healthcare setting and in the patient home.  

 
 

 

 
 

 
 
 
 

 

 
18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 

 

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19 

 

 
 
 

 

 

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Broadbent E, Wilkes C, Koschwanez H, Weinman J, Norton S, Petrie KJ. A systematic 
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Petrie KJ, Cameron LD, Ellis CJ, Buick D, Weinman J. Changing illness perceptions 
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Petrie KJ, Broadbent, E., Meechan, G. Self-regulatory interventions for improving the 
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functional capacity and quality of life (QoL) among elderly people with a chronic disease. 
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26 

CHAPTER TWO: BURDEN OF TREATMENT IN PATIENTS WITH CANCER AND  

MULTIMORBID CONDITIONS: A CONCEPT ANALYSIS 

(Manuscript One) 

 

Introduction 

Patients with cancer encounter a number of challenges as they move through their care 

trajectory. Add one or more other chronic conditions to their cancer, and these challenges are 

likely to multiply. This distress of being a patient can be characterized as the burden of 

treatment. Burden of treatment (BOT) is the combination of the healthcare tasks required to 

manage one’s disease(s) (“workload”), and the individual’s “perspective” of that workload.1 BOT 

is a new and evolving concept that has emerged with the increased prevalence of chronic 

diseases. Due to the increased emphasis placed on self-management of chronic diseases, 

patients with chronic diseases are being asked to complete more healthcare tasks than ever 

before.2-4 The increased responsibility being placed on patients for self-management of their 

treatment is due in part to a paradigm shift of treatment taking place in the home, rather than the 

clinic or hosital.5 This paradigm shift is due in part to financial pressures related to insurance 

coverage and hospital reimbursement for acute-care hospital beds. BOT is a concept that will 

prove to be vital in examining the continuing trend of the added workload to patients’ cancer and 

multimorbid treatment regimens and the challenges experienced by patients to manage their 

cancer and other chronic conditions.  

 

Chronic disease management includes “drug management, self-monitoring, visits to the 

doctor, laboratory tests, lifestyle changes, and other actions that take place in addition to the 

other work patients and their caregivers must do as part of life.”6-8 For cancer patients actively 

receiving treatment, whether infusion in a controlled clinical setting or oral agents in the home, 

there is much that is needed to be done to manage their treatment regimen, to manage 

symptoms and side effects, and to coordinate their care. Cancer is a dynamic disease in that 

the disease status and prognosis change frequently. Because of these frequent status changes, 

treatment regimens must change with the disease status.9  

 

 
27 

Unlike some chronic diseases, such as diabetes or hypertension, cancer patients are not 

able to easily check if their treatment is working, such as monitoring blood glucose and blood 

pressure. This uncertainty of cancer disease status can lead to more distress for patients 

through an increased workload when treatment regimens often require dose modifications or 

unplanned rest periods.10 Changes in disease status and treatment regimens can also bring 

about a new set of symptoms and side effects, which can have an impact on the distress and 

quality of life of cancer patients.9 These changes in distress and quality of life can be positive or 

negative, depending on the prognosis and amount of work that must be done by the patient to 

manage the changing cancer disease status.9   

The addition of another chronic condition to cancer can bring about added tasks and 

may produce added distress for an individual who is already in a challenging situation with their 

cancer. Chronic disease management requires a wide variety, and often a large number, of 

patient tasks. Self-management, which is the behaviors patients engage in order to effectively 

control their conditions(s),11 is considered a vital concept when examining how individuals may 

or may not be successful in controlling their chronic disease(s).12-14 With the shift from hospital 

to home, patient self-management has become more important in examining how individuals 

with chronic disease care for themselves, and what factors may lead to reductions in emergency 

room (ER) visits and hospitalizations. With cancer patients already under a substantial amount 

of distress and having a number of tasks to manage, having another chronic disease to manage 

will likely increase this distress and workload.15  

Another chronic disease not only brings about mental and physical stress through an 

increased workload of healthcare tasks, but individuals can also see a financial burden with the 

addition of a chronic disease.12,14,16-20 In addition to these factors, which a patient may be able to 

control, having a multimorbid condition can be associated with interactions between diseases 

and medications, which patients may be unaware of or have difficulty managing.4,15,21,22 One 

chronic condition, especially cancer, is complex enough in itself, but having another multimorbid 

 

 
28 

condition can have a substantial impact that individuals may not have the knowledge or 

resources to manage the workload and stress that is associated with multimorbidity.  

In the conceptual literature, BOT has been defined as the workload of being a 

patient.”1,6-8,23,24 The three primary contributors to the conceptualization of burden of treatment, 

Tran, Sav, and Eton, present their work in different forms, but come to the following similar 

conclusions: burden of treatment is a multidimensional construct; it is influenced by four primary 

domains of antecedents (patient, social, disease/treatment, and healthcare system); and as 

burden of treatment is increased, patient quality of life decreases and poor patient outcomes 

result.1,6,24 Each of these three research teams cited the normalization process theory (NPT) as 

being part of the foundation for the concept of BOT.1,6,24 NPT is focused on incorporating 

practices into everyday life and sustaining these practices as part of a routine.25 This integration 

of practices can be at an individual or group level. Along with the integration of tasks that need 

to be completed, NPT incorporates “reflexive monitoring,” which is the process of how 

individuals understand the ways in which these practices affect themselves and those around 

them.25 Although NPT incorporates the subjective patient perspective that is involved in BOT, 

little emphasis is placed on patient perspective of workload within the current BOT literature.1,6,24 

When examining burden of treatment, it is important to examine the quantity and 

complexity of the patient’s treatment workload, but also the perspective of how that workload 

may impact the individual. Two individuals with the same number of required tasks may not 

have equal or similar BOT. Each individual’s perspective of their conditions and the required 

tasks must be considered. A negative perspective or greater distress may impact the 

individual’s ability to complete these tasks, and therefore, to properly manage their conditions. 

For this reason, it is crucial to describe an individual’s treatment workload, and then describe 

how this workload may being positively, or negatively impact their daily life. Not every patient 

will have a similar perspective and distress level, even if patients having a similar workload to 

manage. Therefore, it is important to not only examine a patient’s workload, but also the 

 

 
29 

patient’s perspective of their diseases and workload. Examining these perspectives may then 

provide insight into how an individual is able to manage their workload and chronic conditions.   

Purpose 

The purpose of this work is to analyze the concept of BOT, specifically within cancer patients 

with multimorbid conditions, using Rodger’s evolutionary concept analysis method.26,27 This 

concept analysis will comprehensively examine the attributes, antecedents, and consequences 

of burden of treatment within cancer patients with multimorbid conditions in order to establish a 

conceptual model for future research with cancer patients with multimorbid conditions. This 

analysis will lay a foundation for researchers to understand the healthcare tasks required of this 

population, as well as patients’ perspective of this workload. The hope is that future research 

teams will develop interventions aimed at reducing BOT in cancer patients and empowering 

cancer patients with multimorbid conditions to effectively manage their cancer and other chronic 

conditions.  

A concept analysis of burden of treatment within cancer patients with multimorbid 

conditions fills three gaps within the general concept of burden of treatment, but also fills gaps 

specifically within this population. First, this work will emphasize the importance of the 

subjective patient perspective in relation to the workload needed to manage their cancer and 

multimorbid conditions. Minimal research has been conducted examining the workload and 

perspective of cancer patients actively receiving treatment while also having to manage other 

multimorbid conditions.15 Some work has been done with conditions commonly associated with 

cancer, such as diabetes or heart disease, but these studies often examine the multimorbid 

conditions in terms of the outcome variables of interest, such as increased 

hospitalizations.15,21,28 However, these previous works have rarely looked at the subjective side 

of BOT, patient perspective. Second, this work will provide insight not only for researchers, but 

also clinicians who may struggle to understand the challenges that are faced by their patients 

actively receiving cancer treatment while also trying to manage their other chronic conditions. 

 

 
30 

Lastly, this concept analysis will include development of a conceptual model focused on cancer 

patients with multimorbid conditions in order to inform future research and practice that will help 

to ease the daily burden of treatment of this population. This conceptual work will help provide a 

better understanding of BOT, including its attributes, antecedents and consequences, for both 

researchers and clinicians. 

 

Methods 

Rodger’s evolutionary concept analysis method was used to complete this concept analysis.26,27 

Table 2.1. provides the steps of Rodger’s method, as well as the results of this concept analysis 

for each step. This method was chosen, in part, due to Rodgers calling for a particular realm or 

sample to be chosen as the focus of the concept. For this work, cancer patients with 

multimorbid conditions is the sample that fulfills this step of the process. Rodger’s process also 

emphasizes that concepts are dynamic, and that they change over time due to the context in 

which the concept is examined.26,27 Burden of treatment is a concept that has the potential to 

change frequently as the result of a patient’s situation. Therefore, Rodger’s process is a justified 

framework to follow in examining BOT.  

 

 

 

 

 

 

 

 

 

 

 

 

 
31 

Table 2.1. Rodger’s Evolutionary Concept Analysis Process 

Rodger’s Concept Analysis Primary 

Activities 

1. 

2. 

Identify the concept of interest and 
associated expressions (surrogate and 
related terms). 
 
Identify and select the setting and 
sample for data collection. 

3.  Collect data to identify the attributes and 
contextual basis of the concept including 
interdisciplinary, sociocultural, and 
temporal (antecedents and 
consequences) variations. 

4.  Analyze data regarding the 

characteristics of the concept. 

5. 

Identify a model case of the concept. 

6. 

Identify implications and hypotheses for 
further development of the concept. 

 

 

 
 
 
 
 

 
 
 

 

Concept Analysis Results 

1.  Burden of treatment (BOT) – patient workload, 

patient perspective, distress, cognitive 
representation of illness 

2.  Disciplines: medicine, nursing, psychology, 

sociology, public health. 
Databases: PubMed, CINAHL, PsycINFO, 
Cochrane 
Abstract time period: 2013-2017 

3.  Review of the literature and data coding conducted 

for 29 relevant articles.  

4.  Defining attributes: patient workload and patient 

perspective. Antecedents: patient, social, 
disease/treatment, and healthcare system factors. 
Consequences: adherence/non-adherence; clinical 
outcomes; treatment modifications; health and 
well-being. 

5.  A model case was discussed comparing a cancer 
patient with a cancer patient that has one or more 
multimorbid conditions.  

6. 

Individuals with cancer and multimorbid conditions 
are more likely to experience greater BOT. This 
work provides a new roadmap to inform future 
research and practice focused on decreasing the 
daily struggles within this population. 

 
 
 
 

 
 

 
 
 
 
 
 
 

 
 
 

 

A systematic search was performed using the following literature databases: PubMed, 

the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, and the 

Cochrane Library Database. Combinations of MeSH terms and free text key words, having 

similar meanings, were utilized during the search process. Keywords for burden of treatment 

included: burden of treatment, treatment burden, medication burden, disease burden, and 

illness burden. Keywords for cancer and other conditions included: cancer, chronic 

condition/disease/illness, comorbidity/comorbid condition, chronic* and multimorbid*. Additional 

 

 
32 

searches were done using variations of diabetes, cardiovascular disease, and kidney disease. 

These conditions were used specifically by name due to their frequent association with cancer. 

All combination search queries were imputed into all four databases.  

The eligibility criteria for articles were the following: English language, the sample must 

be adults 18 years or older, and studies published within the last 5 years. This time period was 

chosen to be consistent with the use of burden of treatment and to ensure that the included 

literature was as recent as possible. Only original published research articles were included. 

Conference abstracts, editorials, and dissertations/theses were excluded. Articles that utilized 

mortality as the only outcome measure were excluded due to the limited insight this outcome 

measure contributes to burden of treatment. After eligible articles were identified, each article 

was coded for attributes, antecedents, consequences, contextual basis, related/surrogate terms, 

and conceptual/operational definitions. Data were then extracted based on these codes to be 

included in the corresponding sections of the analysis, in order to fulfill Rodger’s process. 

Figure 2.1. shows the Preferred Reporting Items for Systematic Review and Meta-Analysis 

(PRISMA) flow diagram of the reviewed and included articles. In total, 29 full text articles were 

included for data extraction within this concept analysis. Table 2.2. describes some of the 

details of each of the included articles.  

 

 

 

 

 

 

 

 

 

 

 
33 

Figure 2.1. PRISMA Flow Diagram of Reviewed Articles 

 

 

Components of Concept Analysis 

This section provides definition of each of the components of Rodger’s concept analysis 

process, including attributes, antecedents, consequences, contextual basis, related/surrogate 

terms, conceptual/operational definitions, and a model case.27 Attributes are the components 

that make up the concept. For burden of treatment, this would be the two primary components, 

patient treatment workload and the patient perspective of this workload. Antecedents are the 

events or phenomena that precede the concept and may impact the concept. Examples of 

potential antecedents for burden of treatment include individual’s age, marital status, and 

multimorbid conditions. Consequences are the result of the concept occurring or not occurring. 

 

 
34 

An example could be a clinical outcome, such as a hospitalization, or an individual’s quality of 

life. Consequences can be proximal or distal outcomes and have the potential to impact of be a 

part of BOT. For example, a treatment modification can be a consequence, but also a 

component of BOT. When treatment modifications occur as a consequence, the workload and 

BOT can be altered, as well. Contextual basis is the circumstance in which the concept is 

viewed and who it is viewed from. For burden of treatment, this may be the viewpoint of a 

patient with cancer and diabetes, or it could be from the viewpoint of a cardiologist caring for a 

patient with a new diagnosis of cancer.  

Surrogate terms are those words that have a similar meaning to that of the concept and 

may be used to describe the same concept within the literature. For burden of treatment, this 

may be healthcare tasks. Conceptual and operational definitions provide a comprehensive 

meaning of the concept when examining the concept from a theoretical standpoint, as well as 

objectively measuring the concept. A conceptual definition is a theoretical or abstract definition 

of the concept of interest. An operational definition is how researchers measure a specific 

concept or variable. A model case of the concept demonstrates a specific example of the 

concept being utilized within the specific setting or population being studied.  

Results 

Attributes  

As previous burden of treatment literature has discussed, BOT can be broken down into both 

objective and subjective components or measurements. For this work, the objective attribute is 

patient workload, and the subjective attribute is the patient perspective of that workload.  

Patient Workload. As exemplified by the standard definition of burden of treatment, “the 

workload of being a patient,” the concept is mostly viewed for the required tasks that individuals 

must undertake in order to manage their chronic disease(s).1,6,23,24 These activities vary, 

depending on the disease population and the individual patient. From the included articles, 

 

 
35 

patient workload can be further divided into several subcategories: disease and treatment 

demands, interaction with the healthcare system, and financial responsibilities,  

 

The number of treatment-related tasks can be a substantial burden for individuals with 

cancer. This burden is further multiplied when cancer patients have additional chronic diseases 

that require medical management.15 The most commonly cited medication related contributors 

to burden of treatment were having to fill prescriptions, complexity or difficulty with taking 

medications, the total number of medications a patient is required to take for their cancer and 

other diseases, and managing side effects associated with medication regimens.29-39 As the 

number of multimorbid conditions increased, so did the average number of medications that 

patients were required to take.30,31,34,37,38,40 Symptoms and side effects were associated with 

cancer, other chronic conditions, and the medications patients were required to take to manage 

their cancer and other conditions. The management of symptoms and side effects often 

included taking more medications for pain, nausea, vomiting, and infection.31,33,35,39,41 In addition 

to having to comply with medications and treatments to manage cancer and other conditions, 

lifestyle changes were discussed as adding to the patient workload. These lifestyle changes 

included proper dieting, exercise, smoking cessation, decreased alcohol consumption.30,32,42-46 

Similar to the number of medications, as the number of chronic conditions increased, so 

did the frequency with which individuals interact with the healthcare system. Studies that 

evaluated individuals’ interactions with the healthcare system did so by calculating the total 

number of days interacting with the healthcare system, number of providers, and the number of 

visits to a clinic or hospital.33,46,47 While the number of medications and the amount of time an 

individual spends interacting with the healthcare system increases, the financial burden for 

these individuals typically increases as well.33,37,40,43,48-50 Treatment and medications, hospital 

and ER visits, and physician appointments were cited as contributing the greatest burden of 

treatment.33,40,43,48,49 Financial concerns for individuals were more burdensome for those with an 

increased number of multimorbid conditions.44,49 The increased number of conditions and 

 

 
36 

treatment require patients to manage an increased financial responsibility, either by making sure 

their insurance covers the cost or having to pay out of pocket. Financial concerns were related 

both to worrying about maintaining employment to have insurance coverage, paying for 

insurance, and out of pocket (OOP) costs.33,40,43  

Patient Perspective. The patient perspective is how the individual perceives their disease(s) and 

the tasks needed to manage their illnesses (workload). Only three studies included a focus on 

the subjective nature of burden of treatment than the patient workload components.39,41,43 The 

studies that did include insights into individual’s perspective focused on how their disease and 

symptoms impacted their daily life and ability to manage their conditions, individual’s ability to 

cope with their conditions and the tasks brought on by them, and patient’s perception of their 

communication and relationship with providers.39,41,43 One study examined how much symptoms 

bothered patients, and how the number of multimorbidities relates to the symptom severity and 

how bothersome symptoms are.39 They found a correlation between an increased number of 

severe symptoms and an increase in number of multimorbid conditions. Additionally, individuals 

with more multimorbid conditions felt bothered more by their conditions. The odds of feeling 

bothered by a multimorbidity increased as the number of severe symptoms increased.39   

 

The only qualitative study included found that individual’s stress was primarily focused 

around five themes: lack of family support, communication barriers with the healthcare system, 

stress related to be a minority (i.e. racial, gender, or socioeconomic status), caregiver burden, 

and lack of spiritual support.43 Having to manage one or more chronic conditions brought on 

added stress and burden to the individuals included within the study. These stressors made it 

difficult to manage their conditions and daily lives. When asked how they might be able to cope 

with these stressors, individuals identified that access to certain resources, being 

knowledgeable about their diseases and treatment, and having family and support were the best 

ways to cope with the burden. Related to barriers to communication with healthcare providers, 

the final study that included patient perspective examined patient-provider communication by 

 

 
37 

having patients rate their perceived communication with providers.41 They found that cancer 

patients with depression and hopelessness had significantly worse perceived communication 

with their providers. They also pointed out that when patients perceive their communication with 

providers as poor, this can lead to greater psychological distress for patients with cancer. 

Antecedents   

The antecedents included in this concept analysis are placed in four subcategories: patient, 

social, healthcare system, and disease/treatment. As defined earlier, some or all of the 

antecedents must occur before the concept of interest (i.e. BOT) can occur.  

Patient Characteristics. Several patient characteristics were noted as being associated with an 

increased burden of treatment. Additionally, certain patent characteristics were associated with 

individuals that had increased numbers of multimorbid conditions and experienced negative 

consequences within the included studies. Females and older adults typically experienced an 

increased burden of treatment.29-32,43,48,51 Older adults were more likely to have a greater 

number of multimorbid conditions, and therefore have a greater workload than younger adults. 

Additionally, socioeconomic status (SES), race, location of living, and education status played a 

role in the level of burden that patients experienced in some studies.32,35,43,45 This increased 

burden is due in part to the difficulties in access to care of lower SES status, limited insurance 

coverage, and individuals’ proximity to medical facilities.32,35,43,45  

Social Factors. Having a close support system was associated with a lower burden of treatment 

for cancer patients with multimorbid conditions.30,33,35,38,50 These supports systems typically 

consisted of a spouse, family members, or close friends.30,33,35,38,50 A support system can help to 

alleviate some of the workload patients need to complete in order to manage their cancer and 

other conditions. Although having a support system was often associated with a lower BOT, 

some individuals felt they were causing more stress for their support system by having them 

help with daily tasks. These individuals felt that they were a burden to their caregiver or others 

 

 
38 

helping them, which may negatively affect both the patient and those within the support 

system.43   

Disease/Treatment Factors. Disease and treatment factors were often noted as the greatest 

contributors to patients’ overall burden of treatment. For cancer, the factors most greatly 

contributing to BOT were site of cancer, stage, and prognosis. Later stage cancers with poor 

prognoses were associated with greater burden of treatment.29,30,38,49,52,53 For multimorbid 

conditions, certain conditions contributed to an increased burden, including diabetes and kidney 

disease, but the overall number of conditions appeared to have a greater impact on burden of 

treatment.30,38,45-47,49-56 As the number of conditions increased, so did the workload,30,38,45-47,49-56 

and often a worsening perspective on one’s overall BOT.39,41,43 Additionally, psychological 

multimorbid conditions, such as depression, anxiety, and post-traumatic stress disorder (PTSD), 

were associated with a greater BOT.35,36,38,41,43 While these psychological conditions can 

increase one’s workload with appointments with therapists, medications, and other tasks to 

manage the disease, these conditions can also make it difficult for an individual to cope with the 

stress of having to manage cancer and other conditions.35,38,41,43  

Healthcare System Factors. Healthcare system factors are those variables that involve the 

patient to interact with the healthcare system in any way, whether that be hospital stays, ER 

visits, physician appointments, or insurance companies. The size of a hospital, teaching versus 

non-teaching hospitals, and urban versus rural hospitals had an impact on the level of burden 

and certain outcomes for patients.48,57 Individuals being treated at higher patient volume, urban, 

and teaching hospitals were associated with worse outcomes.48 Patients’ access to and type of 

insurance played a role in the level of BOT that patients experienced. As would be expected, 

insurance was closely related to financial burden that patients faced, in terms of being able to 

fulfill certain prescriptions and the OOP costs that were associated with treatment and 

medications.38,43,48-50,55,57 Another aspect of the healthcare system that impacted patients’ BOT, 

particularly their perspective of their situation, was their relationships with providers.41,43 Patients 

 

 
39 

may not feel that their voices are being heard by providers, or that they have received enough 

information regarding medications or treatment.41,43 A strained relationship with providers may 

lead to poor outcomes for individuals.41,43  BOT may be positively, or negatively, impacted by 

some combination of patient, social, disease/treatment, and healthcare system antecedent 

factors. These factors must occur before BOT can happen, and as they change so will the 

patient workload and perspective.   

Consequences 

This work identified several outcomes and consequences within the included studies. The 

outcome of some studies was determined to be patient workload, which is an attribute of BOT, 

while other outcomes were identified as a consequence of BOT occurring. These consequences 

were grouped into four categories: adherence/non-adherence, clinical outcomes, treatment 

modifications, and health and well-being.   

Adherence/Non-adherence. Adherence, or compliance to a medication or treatment regimen, is 

a commonly evaluated outcome for studies including individuals taking medications for chronic 

conditions.58 More multimorbid conditions and a greater number of medications to manage were 

associated with greater non-adherence among individuals with cancer who had other 

conditions.30,40 Not only can an increased workload lead to greater non-adherence, but financial 

burden may impact an individual’s ability to adhere to their treatment regimen.40 Not being able 

to fill prescriptions or having difficulty paying for prescriptions at the proper time can negatively 

impact an individual’s treatment regimen.33,40,49 Not only is adherence a concern for 

medications, but also lifestyle changes, which are vital to many chronic conditions. For 

individuals with both cancer and heart failure, non-adherence to body weight control, reduced 

sodium intake, fluid restriction, and smoking cessation led to poor outcomes.30  

Clinical Outcomes. Clinical outcomes in the included articles covered a wide range of 

consequences, including hospitalizations, readmissions, ER visits, advanced progression in 

disease status, surgical complications, and other adverse events, such as venous 

 

 
40 

thromboembolism (VTE). The majority of included articles that examined clinical outcome 

consequences found that cancer patients with more multimorbid conditions experienced higher 

rates and more severe outcomes than cancer patients with no multimorbid conditions.31,37,45-48,52-

54 Specific to advanced cancer disease status, individuals with cancer and diabetes had 

significantly larger tumor masses than those with cancer alone, according to two studies.42,44 

Post-surgical complications and 30-day readmission rates were higher in individuals with 

multimorbid conditions than cancer alone.46,54 Systemic infections, VTEs, anemia, and skeletal 

events were more likely to occur with multimorbid conditions, especially when having 3+ 

conditions in addition to their cancer.37,45,48,52,53 In general, individuals with more multimorbid 

conditions experienced a greater symptom burden than those with cancer alone.31,33,35,39,41 

Treatment Modifications. Treatment decisions and modifications were common outcome 

variables for several included studies, especially comparing medication regimen modifications 

and treatment plan decisions between individuals with cancer only and those with cancer and 

other multimorbid conditions.29,31,32,34,43,54,57 Treatment modifications were most often the result 

of symptom/side effect profiles, current treatment not working, or a change in an individual’s 

disease status. Cancer patients with multimorbid conditions experienced increased rates of 

symptoms and side effects, which led to more treatment modifications.34,54 Additionally, 

individuals with cancer and multimorbid conditions experienced higher rates of poor clinical 

outcomes, or adverse events, which also led to higher rates in treatment modifications.34,54 

While higher rates of treatment modifications for individuals with cancer and multimorbid 

conditions can contribute to a higher burden of treatment, there were instances noted of 

treatment decisions with these individuals that led to a lower workload.34,54 Two studies found 

that individuals with cancer and multimorbid conditions were less likely to receive certain 

treatments, primarily surgeries, due to having increased risk factors related to their multimorbid 

conditions.32,57 This may decrease the patient workload, but it has the potential to negatively 

impact one’s perspective, if the patient believes they are not receiving the best treatment.  

 

 
41 

Health and Well-being. The final category of consequences primarily focuses on the general 

health of an individual. The included studies evaluated health and well-being with a multitude of 

variables, including quality of life (QOL), health-related quality of life (HRQOL), physical 

functioning, performance status, general health status, and productivity.30,34,35,41,50,51,55 Overall 

health status and quality of life became worse as the number of multimorbid conditions 

increased. Productivity and employment decreased for individuals with cancer and multimorbid 

conditions.50,51,55 These individuals were forced to take more sick leave time and disability pay 

than individuals with cancer alone.55 Whether one views symptoms and side effects as a part of 

clinical outcomes or health and well-being, symptoms have a substantial impact on the QOL of 

individuals. Individuals with cancer and multimorbid conditions more often experienced a greater 

symptom burden, and therefore worse QOL, than individuals with cancer alone.31,50,51  

Burden of Treatment Morbidity (BOTM) Model  

The BOTM Model, developed as part of this dissertation, focuses on the relationships among 

the antecedents, attributes, and consequences of BOT. Much like a mediation model, there is a 

direct relationship between the antecedents and consequences, with the attributes of BOT 

serving as the mediator and therefore helping to explain the relationship between the 

antecedents and consequences. Unlike a traditional mediation model, the BOTM model shows 

feedback loops from from both the attributes and consequences back to the antecedents. The 

feedback loops illustrate that when certain attributes change or a consequence occurs, there is  

potential for impact on some antecedents. Including both the mediation impact of the attribute 

and the feedback loops emphasizes the BOT as a dynamic process. The BOTM model allows 

researchers and clinicians to include antecedents, attributes and consequences that are specific 

to their work in order to answer new research questions or influence new practice changes.   

 

 

 

 

 
42 

Figure 2.2. Burden of Treatment Morbidity (BOTM) Model 

 

 

Discussion 

 

As individuals with cancer continue to live longer, the percentage of individuals with 

cancer and one or more multimorbid conditions will continue to increase. Having to manage 

multiple conditions, especially one as burdensome as cancer, can be a difficult challenge for 

many individuals. For this reason, there is a great need for the examination of the concept of 

burden of treatment within this population. Utilizing the conceptual model and operational 

definition is imperative not only to understand the burden and daily challenges faced by 

individuals with cancer and multimorbid conditions, but also to begin to develop interventions to 

prevent or decrease that burden. This concept analysis is the first to examine the concept of 

burden of treatment specifically within individuals with cancer and multimorbid conditions.  

 

Much like other works that are focused on burden of treatment, this concept analysis 

discussed the dynamic nature of BOT, no matter which chronic conditions an individual has to 

manage. Burden of treatment is impacted by a number of factors, and therefore the attributes of 

 

 
43 

patient workload and perspective have the potential to change often. Some of the antecedent 

factors minimally change, such as age, sex, race, marital status, and SES. While other 

antecedent factors have the potential to change more often, such as certain disease and 

treatment factors. Even if antecedent factors are remaining the same, the attributes of burden of 

treatment have the potential to change frequently. If a new medication is added to a treatment 

regimen for a cancer patient, this can impact interactions with medications prescribed for 

multimorbid conditions, how often patients must interact with their oncology team, the ability for 

the patient manage their cancer and other symptoms, or the perspective of the individual. There 

are many factors that can shift each for these patients. Although complex and difficult to 

evaluate, burden of treatment is a necessary concept to examine for individuals with cancer and 

mulitmorbid conditions. Utilizing the concept of BOT allows researchers and clinicians to 

evaluate the daily challenges faced by this population. 

 

Model Case. For example, take a model case of an older adult male with prostate cancer, 

diabetes, and hypertension. This individual is required to take medications for all three 

conditions, including intravenous (IV) chemotherapy. His latest tumor markers show disease 

progression and that the IV chemotherapy is proving to be ineffective. For this reason, his 

oncologist changes his treatment regimen to an oral anticancer agent that is accompanied by an 

oral glucocorticoid. With this change in disease status and treatment regimen, this individual 

now has a new workload, added stress, and possible negative emotions related to this news. 

Additionally, he is not aware that the newly prescribed glucocorticoid may cause increased 

blood glucose levels, which will require him to adapt how his diabetes is managed. After taking 

the glucocorticoid for more than a week, the patient has been noticing his blood glucose levels 

rising. Upon a visit to his PCP, the patient is frustrated to learn that he was not informed by his 

oncology team that the newly-prescribed medication has the potential to cause increased blood 

sugar levels. While still not providing a full picture, this brief example touches on the 

complexities and challenges faced by an individual with cancer and other multimorbid 

 

 
44 

conditions. As one aspect of his treatment changes, so do several other aspects of his life and 

management of other diseases. Additionally, this example discusses the patient perspective that 

can change as an individual’s workload changes.   

 

As the example illustrates, there a number of factors, both preceding burden of 

treatment (antecedents) and components of BOT, that researchers and clinicians need to focus 

on to better address the challenges faced by individuals with cancer and multimorbid conditions. 

For antecedents, this concept analysis showed that individuals of older age, female sex, SES, 

minority races, lacking a social support system, have advanced cancer, and a greater number of 

multimorbid conditions are faced with a greater overall burden of treatment. This patient 

workload may be a greater number of medications to manage, complex treatment regimens, 

financial burden, increased interactions with the healthcare system, or having to manage 

lifestyle changes. The number of tasks and difficulty in managing them is unique to each 

individual, and therefore each person has a different perspective of the workload required of 

them to manage their conditions. Both the workload and a patient’s perspective have the 

potential to change as antecedents and other events occur. Unfortunately, there were only a 

small number of included studies that had some focus on the subjective nature of burden of 

treatment, which displays a gap in the evidence of patient perspective of cancer patients with 

multimorbid conditions. This concept analysis also depicted that the antecedent factors and 

attributes of burden of treatment have an impact on consequences or outcomes. Those with 

more multimorbid conditions generally experience poorer adherence,30,40 more treatment 

regimen modification,29,54 worse clinical outcomes,45,46,52 and poorer overall health than those 

with cancer alone.33-35  

The results of this conceptual analysis provide a number of research and practice 

implications. Antecedent factors have been identified that researchers and clinicians can target 

to prevent of reduce the burden of treatment and poor outcomes. This work developed a 

conceptual model focused on burden of treatment within the population of individuals with 

 

 
45 

cancer and multimorbid conditions. This concept analysis provides specific categories and 

variables for researchers to include in their work, as well as a visual representation of the 

relationship of antecedent factors, burden of treatment attributes, and consequences. This is not 

to say that all variables discussed within this work should be included within one study. 

Researchers should understand their population of interest and research aims and use this 

conceptual model as a guideline to inform their work. Similar to its implications for researchers, 

this work can help clinicians, whether oncology focused or with expertise in other conditions, 

educate and communicate to patients the importance of certain aspects of burden of treatment. 

Oncologists may also need to alter treatment plans in order to reduce adverse outcomes that 

may result in patients managing their cancer and other conditions. This work also points out the 

vulnerable populations (e.g. older adults, low SES, etc.) that clinicians may need to focus on 

more than others.  

Limitations 

Much like any systematic search, this concept analysis is not without its limitations. The five-

year time period from 2013-2017 was chosen to include the most current literature, while also 

limiting the number of articles to be reviewed. Although this concept analysis included a 

substantial number of articles, having only a five-year time period for inclusion may have 

excluded potential useful studies. Although a few multimorbid conditions that are often 

associated with cancer were specifically included in the search terms, not all conditions were 

searched for by name. This may have limited the studies that resulted from the databases. This 

author decided to excluded articles that only included mortality/survival as an outcome measure. 

The inclusion of these studies may have provided evidence for the impact of cancer and 

multimorbid conditions on long-term survival, but this author felt that it did not add a great deal 

in terms of insight to patients’ burden of treatment. The final limitation is the decision to not 

include studies that focused only on caregivers of individuals with cancer and multimorbid 

conditions. While including these studies would have provided insight to the social support 

 

 
46 

network and workload of patients, the studies did not directly collect data from patients 

themselves and were therefore excluded.   

Conclusions 

This concept analysis and conceptual model are aimed at examining the concept of burden of 

treatment within the population of individuals with cancer and multimorbid conditions. This work 

identified that there are a number of factors that must take place before BOT can occur, and 

that these antecedent factors can have a direct relationship with certain consequences, as well 

as BOT having an impact on that direct relationship. BOT is a complex, dynamic concept that 

can be challenging for researchers to conceptualize, and even more so operationalize. Without 

a strong body of literature, clinicians have a difficult time utilizing BOT and the associated 

variables and outcomes within their practice. This work provides a much-needed 

conceptualization and model for researchers focused on the daily challenges of individuals with 

cancer and multimorbid conditions to better inform research questions and study designs. The 

hope is for this work to lay a conceptual foundation for a program of research that is aimed at 

minimizing the daily burden of treatment faced by this population, as well as to influence other 

researchers to do the same. The goal is to guide future research to answer questions and 

develop interventions to accomplish this goal, so that clinicians are able to have informed 

conversations with patients who struggle to manage both their cancer and multimorbid 

conditions.  

 
 
 

 

 

 
47 

Country 

Study 
Design 
Retrospective 
cohort  
Prospective cohort 

Table 2.2. Study Details for Included Articles (n=29) 
Study 
(Publication 
Date) 
Bayliss et al.  
(2013) 
Chaudhary et 
al. 
(2013) 
Dowling et al.  
(2013) 
Fotos et al. 
(2013) 

Retrospective 
cohort 
Retrospective 
cohort 

Greece 

USA 

India 

USA 

Participants 

539 cancer patients 

358 operable cancer 
patients 

Multimorbid Conditions 

Cardiovascular, hyperlipidemia   

Cardiovascular, hypertension, 
diabetes, etc.  

4,960 cancer survivors, 
64,431 non-cancer 
199 heart failure patients  Hypertension, CKD, chronic 

Cardiovascular, diabetes, and 
MEPS priority conditions 

Khorana et al. 
(2013) 
McLean et al. 
(2013) 

Antczak et al. 
(2014) 
Hart et al.  
(2014) 
Islam et al.  
(2014) 

Kayser et al.  
(2014) 

Sarfati et al.  
(2014) 
Baz et al. 
(2015) 

Gershon et al. 
(2015) 

Gilmelius et al. 
(2015) 
Ording et al. 
(2015) 
Østgård et al. 
(2015) 
Thekdi et al.  
(2015) 
Blondeaux et al. 
(2016) 

Gheihman et al. 
(2016) 
Guddati et al. 
(2016) 
Huang et al.  
(2016) 
Jim et al.  
(2016) 
Kleeff et al. 
(2016) 

USA 

Ireland 

Canada 

USA  

Australia 

USA 

New 
Zealand 
USA 

Canada 

Sweden 

Denmark 

Denmark 

USA 

Italy  

Retrospective 
cohort 
Retrospective 
cohort 

Retrospective 
cohort 
Retrospective 
cohort 
Cross-sectional 
survey 

Qualitative focus 
groups 

Retrospective 
cohort 
Semi-structure 
qualitative 
interviews 
Retrospective 
cohort 

Retrospective 
cohort 
Retrospective 
cohort 
Retrospective 
cohort 
Baseline data from 
RCT 
Cross-sectional 

Canada 

Cross-sectional 

USA 

USA 

USA  

UK 

Retrospective 
cohort 
Retrospective 
cohort 
Cross-sectional 

Retrospective 
cohort 

17,284 cancer patients, 
17,284 non-cancer 
52 palliative care cancer 
patients 

144,889 RCC with bone 
metastasis  
488 pancreatic cancer  

4,574 older adults 

Chronic disease patients, 
caregivers, and 
healthcare professionals 
524 cancer patients 

20 multiple myeloma 
patients 

7,241,591 COPD 
patients 

1,989 HL patients,  
7956 non-cancer 
44,035 prostate cancer, 
213,810 non-cancer 
2792 AML patients 

respiratory failure, cancer, 
mental illness  
Charlson Comorbidity Index 

Hypertension, cardiovascular, 
diabetes, dyslipidemia, COPD, 
depression, arthritis 
Charlson Comorbidity Index 

Diabetes  

Cancer, hypertension, chronic 
respiratory, depression, 
diabetes, stroke, cardiovascular 
Cancer, cardiovascular, 
HIV/AIDS 

C3 comorbidity index  

Not-specified  

Cancer, cardiovascular, 
diabetes, musculoskeletal, 
mental illness 
Cardiovascular diseases  

Charlson Comorbidity Index 

Charlson Comorbidity Index 

287 RCC patients 

Depression, PTSD 

Cardiovascular, diabetes, 
hypertension  

200 breast cancer 
treated with AIs,  
200 non-cancer 
341 acute leukemia 
patients 
49,515 metastatic cancer  Cardiovascular diseases 

Depression 

78,338 breast cancer  

1,869 HCT-recipients 

Diabetes, cardiovascular, 
hypertension, obesity 
Depression 

1,105 pancreatic cancer 

Diabetes 

 

 
48 

Country 

Japan 

USA 

Table 2.2. (cont’d) 
Study 
(Publication 
Date) 
Maeda et al.  
(2016) 
Neugut et al. 
(2016) 
Presley et al. 
(2016) 
Guy et al. 
(2017) 
Hawkins et al. 
(2017) 
Ritchie et al. 
(2017) 

USA 

USA 

USA 

USA 

Participants 

152 breast cancer 

21,255 breast cancer 

Study 
Design 
Retrospective 
cohort 
Retrospective 
cohort 
Retrospective 
cohort 
Retrospective 
cohort 
Retrospective 
cohort 
Secondary analysis  3,016 cancer patients 

10,293 cancer survivors, 
135,151 non-cancer 
3,184 cancer survivors, 
44,997 non-cancer 

7,955 NSCLC 

Abbreviations: MEPS, Medical Expenditure Panel Survey; CKD, chronic kidney disease; COPD, 
chronic obstructive pulmonary disease; RCC, renal cell carcinoma; C3, Cancer, Care and 
Comorbidity; HL, Hodgkin’s lymphoma; AML, acute myeloid leukemia; PTSD, post-traumatic 
stress disorder; HCT, hematopoietic cell transplant; ICD, International Statistical Classification 
of Diseases and Related Health Problems 
 

 

Multimorbid Conditions 

Diabetes 

Charlson Comorbidity Index 

Quantified from ICD-9 

MEPS priority conditions 

Cardiovascular, hypertension, 
stroke, diabetes, arthritis 
Derived based on chronic 
condition medications using 
RxRisk 

 

 
49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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thromboembolism: A cohort study of 44,035 Danish prostate cancer patients, 1995-2011. 
Cancer. 2015;121(20):3692-3699. 

Chaudhary RK, Bhaduri D, Bhatia M, Hatti S, Ba R, Meva J. Influence of comorbidity in cancer 
surgery on treatment decisions, postoperative course and oncological outcome. Asia-Pacific 
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Glimelius I, Ekberg S, Linderoth J, et al. Sick leave and disability pension in Hodgkin lymphoma 
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55 

CHAPTER THREE: HEALTHCARE SYSTEM CHALLENGES IN PATIENT ACQUISITION OF 

ORAL ONCOLYTIC AGENTS 

(Manuscript Two) 

Introduction 

 

A paradigm shift in cancer treatment has led to patients being more responsible for the 

management of their cancer care.1-5 The continued upward trend of oral oncolytic agent (OOA) 

prescriptions means patients are being given intravenous (IV) chemotherapy in a controlled 

clinical setting less frequently and take chemotherapy orally in the home instead. Although 

OOAs provide a new convenience for patients, different challenges arise with an OOA 

prescription, including symptom and medication management.6,7 A major challenge in a new 

OOA prescription is getting the OOA into the patient’s hands, or patient prescription acquisition. 

Unlike patients going into a clinical setting to receive IV chemotherapy, patients must either 

receive OOA prescriptions through the mail or from a specialty or hospital pharmacy. The 

process of patients acquiring OOAs involves a number of steps that could prevent acquisition of 

oral agents in a timely manner. This process includes provider prescription, insurance 

authorization, completion of financial assistance, pharmacy fulfillment, and delivery of OOAs to 

the patient home.8 Cancer patients prescribed OOAs are at a critical point in their care 

trajectory, as most patients have late-stage solid tumors and have received multiple lines of 

treated prior to being prescribed OOAs.9,10 For this reason, it is imperative that patients receive 

their OOAs promptly following a provider’s prescription.11 However, patients face numerous 

challenges to prompt receipt of their oral agents.  

Financial barriers continue to be one of the most common issues that patients face in 

acquiring their OOAs in a timely manner.7 The patients’ primary insurance must seek insurance 

approval first, followed by any secondary insurances. At this point, cancer patients still may not 

be able to afford the copays after insurance coverage, if the OOAs are covered at all.8,12,13 Oral 

oncolytic agents are billed to insurance differently than IV chemotherapy because they are 

considered a prescription medication and not part of a clinic or hospital visit as is the case with 

 

 
56 

IV. Thus, for many insurances, OOAs are billed within pharmacy benefits, which may result in 

greater out-of-pocket (OOP) costs than IV chemotherapy with medical treatment coverage. 

Some insurance companies are not covering full OOA prescriptions, or even the majority of the 

cost, due to rising costs and a lack of evidence of consistent effectiveness, especially with oral 

agents only recently approved by the FDA.14,15 Because of this, patients may need to turn to 

financial assistance programs through their care system or the drug company that sells the 

OOA.8,13 These programs may only be a temporary fix for patients to afford and acquire their 

OOAs. Going through the process of insurance approval and seeking financial assistance can 

range from a couple days up to multiple weeks.8,11,13,16 

Once the medication is approved by insurance, or financial assistance is arranged, 

obtaining an OOA prescription is not as simple as having it filled at the local drug store. Oral 

oncolytic agent prescriptions are typically filled through a hospital or institution pharmacy or 

through a specialty mail-order pharmacy.8 Specialty pharmacies may experience some delays 

because they need specific approvals from both prescribers and insurance providers for specific 

medication, in addition to the wait time for the medication to be shipped to the patient’s home 

with the possibility of shipping delays.17 When an OOA is prescribed to cancer patients at a 

vulnerable state, treatment delays can be problematic for their quality of life, as well as for the 

overall disease prognosis.  

Once patients are able to acquire their initial OOA prescription through this occasionally 

complex process of navigating financing programs and multiple healthcare system personnel, 

barriers may still arise acquiring subsequent refills.8 Oral oncolytic agent protocols often involve 

dose reductions, treatment delays, and medication or treatment changes, which may increase 

the chance of prescription errors, either by the prescriber or the pharmacy filling the 

prescription.12 If a patient is receiving regular shipments of their OOA from a specialty pharmacy 

(i.e., on schedule with the original treatment protocol timeline), the pharmacy may not be 

updated when regimen changes are made, which may lead to patients receiving incorrect 

 

 
57 

dosages or receiving their OOA at the wrong time.11 These challenges from initial, and 

subsequent, prescriptions ultimately affect the patient. Patients often have more responsibility 

with OOA prescriptions than with traditional IV chemotherapy, including adherence, symptom 

management, other medication management, and financial management for treatment.1,2,4,6,7 

These healthcare system issues influence the patient in a way that is completely out of their 

control and may increase their workload and their overall distress. The healthcare system 

issues this work will examine include insurance delays, prescription errors, pharmacy errors, 

shipment delays, and any other situation identified by patients that caused a delay in receiving 

their OOA prescription.   

Purpose 

The purpose of this work is to identify the impact of the healthcare system on patient acquisition 

of OOAs. This work is a secondary analysis of a parent intervention trial focused on cancer 

patients newly prescribed OOAs, evaluating the time from prescription to patient acquisition. 

Two specific research questions guided this secondary analysis: 1) what is the time from 

prescription to receipt of OOAs for patients in the parent intervention study; and 2) how might 

type of insurance, OOA copay, patient recruitment site, and oral oncolytic drug class predict the 

time from prescription to receipt of OOAs? Guided by the Burden of Treatment Morbidity 

(BOTM) Model (Figure 1), the focus of this dissertation, this exploratory work examined the trial 

population for prescription acquisition challenges identified within the literature.  

Conceptual Model 

The Burden of Treatment Morbidity (BOTM) Model guides this chapter. An adaptation of the 

overall dissertation model will highlight the specific components of the BOTM being used 

(Figure 3.1). The adaptation of the BOTM model utilizes the two antecedent categories 

(disease/treatment factors and healthcare system factors) and the consequence of patient OOA 

acquisition. Oral oncolytic agent class is included for disease/treatment factors, and recruitment 

site, insurance type, and OOA copay are included for healthcare-system factor antecedents. 

 

 
58 

The primary outcome of this work is the patient time to acquisition from initial OOA prescription. 

Because this work is looking at the direct predictive influence of the antecedents on the 

consequence, the moderation of burden of treatment was excluded from the BOTM adaptation 

for this chapter.  

 

Figure 3.1. Burden of Treatment Acquisition Analysis Model 

 

Methods 

This work was a secondary, longitudinal data analysis of a randomized-controlled trial that 

tested adherence and symptom management interventions in cancer patients that had been 

newly prescribed OOAs. The parent study, Improving Adherence to Oral Cancer Agents and 

Self Care of Symptoms Using an IVR, funded by the National Cancer Institute (1Ro1CA162401-

O1A1), was an eight week two-arm, multisite randomized controlled trial (RCT).18 with 

concluding observations at 12 week to determine sustainability. The experimental arm received 

a weekly symptom assessment with referral to a symptom management toolkit for symptoms 

≥4/10, and an adherence intervention comprised of daily reminders, using an automated 

 

 
59 

interactive voice response (IVR) system. Control patients received standard care without 

referrals to a toolkit or reminder calls.  

Sample 

Parent Study Sample. Participants were recruited from six NCI-designated comprehensive 

cancer centers across the Midwest United States. Patients agreed to enroll in the study by 

signing a consent form, and could withdrawal at any point during the study. Inclusion Criteria for 

the parent study: Patients were to be 21 years or older; speak English; newly prescribed one of 

the designated FDA-approved oral oncolytics (Appendix A); Eastern Cooperative Oncology 

Group (ECOG) performance of 0-2 or Karnofsky score of ≥50 (Appendix B)19,20 and actively 

receiving cancer treatment from one of the participating cancer centers. Exclusion Criteria: 

Patients were excluded if they had difficulty hearing on the telephone; limited or no access to a 

touch- tone phone; cognitive deficits, as determined by recruiters; or receiving hospice care.  

  

In the parent study, 482 patients were screened for eligibility, with 438 being eligible, and 

322 of these patients consented for the study. Of the consented patients, 272 patients 

completed the baseline interview and were then randomized into one of the two arms (Appendix 

C). Institutional review boards (IRB) at the study center and each recruitment site approved the 

study and all protocols. 

Data Collection. Data were collected over the phone by trained interviewers at baseline, 4, 8, 

and 12 weeks. Interviewers collected information including OOA prescription information and pill 

counts, demographics, insurance coverage, social support, symptoms, multimorbid conditions, 

and interactions with healthcare providers. Data were also collected during the experimental 

group’s daily reminder calls, as well as at the automated weekly, symptom calls. After patients 

completed the study, or attrited, trained abstractors at the respective cancer centers completed 

medical record audits (MRA) (Appendix D). Quality assurance checks were undertaken for 

every MRA, as well as randomly selected interviews each month for all patient interviewers. 

 

 
60 

Secondary Data Sample. All 272 participants that completed the baseline interview of the parent 

study will be included in this secondary data analysis. Participants will not be differentiated by 

experimental/control groups, since the intervention did not influence the outcome of time to 

acquisition of OOAs.  

Measures 

Table 3.1. includes an explanation of all included variables and measures. Patient demographic 

information was collected at enrollment and the baseline interview; this included age, sex, race, 

educational level, employment, and marital status (Appendix E). These demographic variables 

used will provide an overview of the sample of the secondary analysis. The primary variables 

used in this work are information gathered from both the patient telephone interviews and the 

MRAs. These variables include OOA drug class, insurance type, recruitment site, whether or not 

participants had an OOA copay, and the time from prescription to receipt of OOAs. All of the 

predictor healthcare system variables used in the model are categorical, including: OOA drug 

class (4 levels), insurance type (2 levels), recruitment site (5 levels), and OOA copay (2 levels). 

The outcome variable of the model was the acquisition of the OOA. This was the patient 

report of the number of days from initial OOA prescription until the patient received the OOA in 

his/her hands. Days to receipt is a continuous variable for this work. Some patients were unable 

to recall the number of days from prescription to receipt. For these cases, the prescription date 

from the MRA to the patient reported date for when the participant started taking the OOA 

equates the days to receipt.  

 

 

 

 

 

 

 

 
61 

Table 3.1. Secondary Data Analysis OOA Acquisition Variables 

Construct 

Measures 

Age, Sex, Race, 
Education Level, 
Employment, and Marital 
Status 

Patient 
Demographics 
Disease/ 
Treatment 
Factors 
Healthcare 
System Factors  Primary Insurance Type 

OOA Medication Class 

Description 

Patient reported during screening and baseline 
interview 

Four categories: cytotoxics, kinase inhibitors, sex 
hormone inhibitors, other 

Two categories: government or commercial/private 

Oral Oncolytic Out of Pocket 
(OOP) Cost 

Yes/No 

Patient Recruitment Site 

Time to Initial Acquisition 

Five categories: Site A, Site B, Site C, Site D, and 
other  
Number of days between the initial OOA prescription 
and when patient received the OOA (continuous) 

 

 
Outcome 
Variable  

 

Data Analysis 

Frequencies of sample characteristics were summarized with descriptive statistics. In order to 

standardize the data and reduce skewness, a square root (sqrt) transformation was used for the 

outcome variable, days to receipt. Descriptive statistics were used to report the unadjusted 

means for the initial values of days to receipt and square root values of days to receipt. These 

means were reported by the healthcare system factor variables and OOA drug class.  

 

To answer research question 2, how the healthcare system variables might predict the 

days to receipt of OOAs, a three-way ANOVA model was used. All two-way interactions and a 

three-way interaction between OOA drug class, OOA copay, and insurance type were included. 

Patient recruitment site was also included in the model as a main effect. Three-way and two-

way interactions were examined for statistical significance at 95% level. F-values were 

calculated for simple interaction effects of OOA copay and insurance type by splitting the file 

and examining the interaction effects by each OOA drug class. In order to calculate the new f-

values, the new mean squares (MS) were divided by the MS for the error term from the original 

 

 
62 

ANOVA model. These f-values were then tested for significance based on degrees of freedom 

(df) for simple interaction and total df for each OOA drug class within the split file model.    

Results 

The secondary analysis sample included all 272 participants from the parent trial that completed 

the baseline interview. The sample was evenly split between males and females and had a 

mean age of 61 years (Table 3.2.). Breast and colorectal were the two most common sites of 

cancer within the trial, comprising 36% of the sample. Leukemia was the most common of the 

hematologic malignancies (leukemia, lymphoma, and myeloma). Fifty-seven percent of the 

sample’s primary form of insurance was government coverage, which included Medicare, 

Medicaid, and Veterans Affairs (VA). The rest of the sample’s insurance coverage primarily 

consisted of self or spouses’ coverage through their current or former employer. At intake, 53% 

of the sample had an out of pocket (OOP) copay for their initial OOA prescription, with a mean 

cost of $258 (range $1-8300 USD). Of the 28 primary oral agents patients were prescribed, 82% 

were taking either a cytotoxic or a kinase inhibitor at the start of the trial. The University of 

Michigan Cancer Center was the largest recruitment site for the trial, making up over 41% of the 

sample that completed the intake interview. 

 

The mean number of days from prescription to receipt of patients’ initial OOA 

prescription was 9.73 days (range 0-135). Patients with private/commercial insurance had to 

wait on average 9.9 days to receive their initial OOA prescription, while those with government 

insurance had an average acquisition time of 9.61 days (Table 3.2.). Patients without an OOA 

copay had a slightly longer wait time than those with a copay, 10.32 and 9.47 days, 

respectively. Patients prescribed other OOAs or kinase inhibitors experienced longer wait times, 

11.75 days and 10.54 days, respectively. Those patients treated at recruitment site D waited on 

average 12.79 days to receive their OOAs, while all other cancer centers saw averages below 

the sample mean (9.73). With the range of days to receipt being quite large, a square root 

 

 
63 

transformation was implemented to create the square root (Days to Receipt) variable. The 

resulting square root sample mean was 2.56 days (SD 1.73). 

 

A three-way analysis of variance (ANOVA) was conducted on the influence of healthcare 

system factor independent variables and OOA drug class on the number of days to receipt of 

OOAs (Table 3.4.). The three-way interaction between OOA copay, insurance type, and OOA 

drug class yielded a significant result, F (3, 232) = 3.612, p .01. Given the significant three-way 

interaction, simple interaction effects of OOA copay and insurance type by OOA drug class 

were analyzed. The following F values resulted: cytotoxics, F (1,78) = 0.548, p .46; kinase 

inhibitors, F (1, 114) = 6.709, p .01; sex hormone inhibitors, F (1, 19) = 7.879, p .01; and other, 

F (1,11) = 0.417, p .06. Additionally, there was a significant main effect of insurance type on 

sqrt(days to receipt), F (1,232) = 5.445, p .02.  

Figure 3.2 displays the plots of simple interaction effects of OOA copay and insurance 

type on sqrt(days to receipt) by OOA drug class. In addition to the unadjusted initial values of 

days to receipt and unadjusted sqrt(days to receipt), Table 3.3. shows the estimated marginal 

means (least square means) for each level of the healthcare system factor independent 

variables. When taking into account the other healthcare system variables, there were slight 

changes in the estimated marginal square root means from the unadjusted square root means. 

Most notably, patients having government insurance coverage saw a longer number of days to 

receipt of OOAs when taking into account of variables within the ANOVA model.    

Discussion 

Advances in cancer treatment have led to a shift in the setting of the delivery of cancer care. As 

opposed to chemotherapy being administered via IV in a controlled setting, the increased 

prescription of OOAs have allowed more cancer patients to self-administer their chemotherapy 

in the home. Although OOAs provide some advantages for patients, OOAs are not without their 

drawbacks. With a substantial number of OOAs being approved by the FDA within the last 

years, insurance approval and the restrictions on facilities that are able to fill OOA prescriptions 

 

 
64 

have become challenges for providers and patients. This secondary analysis showed that 

cancer patients had to wait on average nearly 10 days from the time of provider prescription 

until they received their OOA. Nearly 23% of the sample received their OOA prescription on the 

day it was prescribed or the next day. Twenty-six patients had to wait three weeks or more to 

receive their medication, with one patient waiting 135 days. This wait time suggests that there 

may be barriers to patients receiving their OOA prescriptions in a timely manner. Insurance 

approval and specialty pharmacy fill times have been cited in previous works as primary 

contributors to the wait time of cancer patients in receiving their OOAs.8,11,13,16 

 

  The process from oncology provider prescription of OOAs to when the patient receives 

the medication and can begin treatment is a bit different from traditional IV chemotherapy. 

Unlike IV chemotherapy, which is billed under medical, OOAs are billed under the pharmacy 

benefit portion of patients’ insurance coverage.16 After provider prescription of an OOA, the 

oncology clinic or hospital must submit the necessary documentation the patient’s insurance for 

approval. The amount of time for insurance approval may differ by type of insurance, insurance 

provider, and state in which the patient is receiving treatment.8,13,14,21 This study found that 

insurance type might influence the time to receipt of OOAs, depending on whether or not 

individuals had a OOP copay and the OOA drug class that was prescribed. After insurance 

approves patients’ OOAs, they may or may not have an OOP expense to cover the prescription. 

With several OOAs costing in excess of $100,000 (USD) per year before insurance,22 cancer 

patients may take on a financial burden to cover the OOA copay. 

 

Although more than 50% of the sample reported having an OOP copay, this research 

team believes that the number of patients that had some cost after insurance approval was 

higher. The reason for this is that many patients received financial assistance, either patient-

assistance programs offered by drug companies that sell their specific OOA. The sample mean 

copay was $258 (USD), however the median copay was only $25. Dependent on insurance type 

and OOA drug class, OOP copay influenced the time to receipt of OOAs within this study. Given 

 

 
65 

the large pre-insurance costs of most OOAs, this research team believes that, along with 

insurance coverage, financial assistance provided a great relief for patients in either reducing or 

eliminating OOP copays. Unfortunately, patients may not be able to receive financial assistance 

for the duration of their treatment, and at some point, will have to make difficult financial 

decisions.  

Not all patients found it feasible to pay for the OOP cost of their OOA prescription. During 

patients interviews within the parent trial, some patients discussed having to make difficult 

decisions in order to pay for their OOAs. Some patients mentioned having to borrow from their 

retirement funds or social security, borrowing money from a loved one, and taking out additional 

loans. Other patients stated that they or a spouse had to continue working in order to maintain 

insurance coverage or a viable income, even though they would have preferred to leave the 

workforce.   

 

  After receiving insurance approval and finding a way to pay for their OOAs, cancer 

patients need to find a way to get the medication in their hands. OOA prescriptions cannot be 

filled as simply as other chronic condition medications. Oral agents can only be distributed by  

on-site hospital pharmacies, specialty pharmacies, and mail-order pharmacies.8,13 If patients are 

being treated at a larger medical center, they may be able pick up their prescription with each 

oncology visit, but a substantial number of patients rely on mail-order pharmacies to fulfill their 

OOA prescriptions. Being treated at a hospital or cancer center with pharmacies that fill OOA 

prescriptions can drastically reduce the patient acquisition time. This may have been a 

contributor to the longer acquisition times for patients treated at Site D.  

In a previous study, pharmacy fulfillment and delivery of OOAs accounted for the longest 

portion of the OOA acquisition process.8 This secondary analysis did not have data specific to 

each component of the OOA acquisition process, but interviewers were able to note examples 

of patients encountering delays with pharmacy fulfillment and delivery during the parent trial. 

Some patients reporting receiving the wrong dose or wrong label on the medication bottle, 

 

 
66 

having the wrong name on the package, receiving their medication after the expected date, and 

concerns were expressed about expensive medications being left out in the open when 

delivered. These situations contribute to the delay in starting new treatment or continuing on 

vital cancer treatment.  

Limitations 

This secondary analysis was limited in the following ways, due to the available data from the 

parent trial. First, the outcome measure of days to receipt of OOAs was based on patient report. 

Although this research team believes the data to adequately depict the experiences of the 

patients in the trial, not all patient accounts may have been accurate in recalling the days from 

prescription to receipt. Another limitation of the data is only having one endpoint to describe the 

full acquisition process, as opposed to being able to break down the number of days for each 

phase of the process. Participants were recruited from NCI-designated comprehensive cancer 

centers across the Midwest United States, and therefore, may not reflect the experiences of 

patients treated at clinics or hospital systems from other parts of the country or that are not 

comprehensive cancer centers. This study did not take into account whether patients were 

fulfilling their OOA prescription through a specialty pharmacy, at their medical center/clinic, or 

through a mail-order pharmacy. Finally, this secondary analysis only described the time to 

receipt of the initial OOA prescription. Most patients are prescribed OOAs indefinitely, so 

additionally work is needed to look at times to receipt of subsequent refills.  

Conclusions  

The purpose of this study was to determine the length of time cancer patients waited to receive 

their OOAs from the time of initial written prescription, and to determine what factors might 

influence this time to drug acquisition. This study showed that the amount of time patients must 

wait before receiving their initial prescription might vary depending on their type of insurance 

and whether or not they had a copay. Patients’ treatment location and where they fulfill their 

prescription may also play a role in their time to acquisition. Oncology nurses should 

 

 
67 

communicate with patients that they might be waiting some time to receive their OOAs after the 

initial prescription. Oncology nurses should understand the process of fulfilling an OOA 

prescription, so that they are able to educate patients to help expedite the process or to identify 

when issues arise in the acquisition of OOAs. Nurses and other providers should also follow up 

with patients at each oncology clinic visit to ensure that patients are not encountering any issues 

with subsequent refills. In addition to communicating patients, providers should be 

communicating with one another, insurance companies, OOA manufacturers, and pharmacies 

to ensure prompt delivery to patients. Such communication may be prescriber to nurse at the 

time of prescription to begin documentation as early as possible, as well as providers following 

up with insurance companies and outside pharmacies to speed up portions of the process.   

Future work should examine each phase of the OOA acquisition process and continue to 

follow up with patients through subsequent refills. This work would allow researchers and 

providers to identify which phases of the acquisition process account for the greatest amount of 

time. Additionally, an intervention trial should be conducted testing the use of a clinical liaison, 

such as an oncology nurse, to help guide cancer patients through the acquisition process and 

maintain frequent contact with patients to assist if issues were to arise. This work may help to 

enact policies that regulate each step of the OOA acquisition process, in order to promptly 

deliver OOAs to cancer patients. These policies could lead to changes in documentation or 

staffing at certain phases of the process, which may work to expedite the acquisition process. 

These changes may focus particularly on insurance coverage and payments, as well as how 

specialty pharmacies and clinics work with insurance companies and patients to fulfill OOA 

prescriptions. Cancer patients prescribed OOAs, particularly solid tumor patients that have gone 

through several forms of treatment, are at a critical point in their care trajectory. Future research 

and practice need to work to delivery patients proper care in a timely manner. Not receiving their 

OOAs in a timely manner can not only be frustrating for patients, but not being able to start 

 

 
68 

treatment or continue treatment as prescribed may lead to poor disease control and worsening 

outcomes for patients. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
69 

Table 3.2. Descriptive Statistics for the Secondary Analysis Study Sample (N=272) 
Characteristic 
Age (Mean Years, SD) 
Sex 

Frequency n (%)                                   

Male  
Female 

Race 

African American  
Caucasian  
Other/unknown 

Level of education 
High school or less 
Some college or completed college 
Graduate or professional degree 
Unknown 

Employment status 

Employed 
Unemployed 
Marital status 

Married 
Not married 

Insurance type 

Government 
Private/Commercial 

Oral agent copay 

Yes 
No 
Don’t know 

Recruitment site 

Site A 
Site B 
Site C 
Site D 
Other 

Drug category 

Cytotoxic agents 
Kinase inhibitors 
Sex hormone inhibitors 
Other 

Site of Cancer 

Brain 
Breast  
Colorectal 
Esophageal 
GI 
Leukemia 
Liver 
Lung 
Lymphoma 
Melanoma  
Myeloma 
Pancreatic 
Prostate 
Renal 
Sarcoma 
Other 

 

61.38 (12.22) 
 
136 (50) 
136 (50) 
 

             22 (8) 
241 (89) 
 7 (3) 

 
71 (26) 
150 (55) 
49 (18) 
2 (1) 

 
88 (32) 
184 (68) 
 
167 (61) 
105 (39) 
 
154 (57) 
118 (43) 
 
143 (53) 
114 (42) 
15 (5) 

 
41 (15) 
111 (41) 
37 (14) 
58 (21) 
 25 (9) 

 
95 (35) 
127 (47) 
27 (10) 
                                            23 (8)    
 

     2 (1) 
57 (21) 
41 (15) 
     3 (1) 
17 (6) 
16 (6) 
12 (4) 
10 (4) 
  3 (1) 
  8 (3) 
  7 (3) 
  27 (10) 
  26 (10) 
24 (9) 
15 (5) 
  4 (1) 

 
70 

Table 3.3. Unadjusted and Adjusted Mean Days to Receipt of OOAs by Healthcare System 

Unadjusted Mean 
(SD) 

Unadjusted Sqrt Mean 
(SD) 

Estimated Marginal 
Mean Sqrt (SE) 

 
2.53 (1.80) 
2.69 (1.63)  
 
2.65 (1.56) 
2.56 (1.95) 
 
2.69 (1.27) 
2.65 (1.09) 
3.15 (1.72) 
2.30 (2.02) 
2.48 (1.46) 
 
2.46 (1.45) 
2.65 (1.88) 
2.90 (1.29) 
2.49 (2.38) 
2.56 (1.73) 

 
2.96 (0.22) 
2.22 (0.24) 
 
2.66 (0.21) 
2.53 (0.25) 
 
2.74 (0.31) 
2.48 (0.33) 
3.10 (0.27) 
2.30 (0.22) 
2.40 (0.38) 
 
2.67 (0.22) 
2.61 (0.17) 
2.11 (0.44) 
2.98 (0.42)  
2.59 (0.17) 

Factors  
Healthcare System 
Factors 
Insurance Type 
Government 
Private/Commercial 

OOA Copay 

Recruitment Site 

Yes  
No 

Site A 
Site B 
Site C 
Site D 
Other 

OOA Drug Class 

Cytotoxic 
Kinase Inhibitors 
Sex Hormone Inhibitors 
Other 

 
9.61 (15.70) 
9.90 (13.11) 
 
9.47 (12.84) 
10.32 (16.98) 
 
8.83 (6.98) 
8.21 (5.78) 
12.79 (13.23) 
9.32 (19.39) 
8.21 (7.45)  
 
8.15 (9.30) 
10.54 (15.63) 
10.00 (6.13) 
11.57 (28.67) 
9.73 (14.54) 

Total 
Abbreviations: OOA, oral oncolytic agent; SD, standard deviation; SE, standard error 
 
Table 3.4. ANOVA Results for Effect of Healthcare System Factors on Days to Receipt of OOAs  
 
Variable 
Recruitment Site 
OOA Copay 
Insurance Type 
OOA Drug Class 
Copay*Insurance Type 
Copay*Class 
Insurance Type*Class 
Copay*Insurance Type*Class 

Mean Square 

df 

F 

 
4.666 
0.430 
15.280 
1.877 
34.217 
3.539 
8.470 
10.134 
2.806 
 

 
1.663 
0.153 
5.446 
0.669 
12.195 
1.261 
3.019 
3.612 
 
 

P 

 
0.15 
0.69 
0.02 
0.57 
<0.01 
0.29 
0.03 
0.01 
 
 

 
4 
1 
1 
3 
1 
3 
3 
3 
232 
252 

Error 
Total 

Abbreviations: OOA, oral oncolytic agent; df, degrees of freedom 
 
 
 

 

 

 
71 

Figure 3.2. ANOVA Simple Interaction Effects Plots on OOA Drug Class 
 
 

 

 

Cytotoxics 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 

Kinase Inhibitors 

 

 

 
72 

Figure 3.2. (cont’d) 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

Sex Hormone Inhibitors 

 
 

Other 

 
 

 

 
73 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

APPENDICES 

 

 
74 

 

 

 

APPENDIX A: FDA Approved Oral Oncolytic Agents Included in the Parent Study 

 
Table 3.5. FDA Approved Oral Oncolytic Agents Included in the Parent Study 

Oral Oncolytic Agents Organized Alphabetically by Trade Name 

Afinitor (Everolimus)  
Bosulif (Bosutinib)  
Gilotrif (Afatinib)  
Gleevec (Imatinib)  
Ibrance (Palbociclib)  
Imbruvica (Ibrutinib)  
Inlyta (Axitinib)  
Lenvima (Lenvatinib)  
Lonsurf (Tipiracil & Trifluridine)  
Lynparza (Olaparib)  
Nexavar (Sorafenib)  
Pomalyst (Pomalidomide)  
Revlimid (Lenalidomide)  
Sprycel (Dasatinib)  

Stivarga (Regorafenib)  
Sutent (Sunitinib)  
Tafinlar (Dabrafenib)  
Tarceva (Erlotinib)  
Tasigna (Nilotinib)  
Temodar (Temozolomide)  
Tykerb (Lapatinib)  
Votrient (Pazopanib)  
Xalkori (Crizotinib)  
Xeloda (Capecitabine)  
Xtandi (Enzalutamide)  
Zydelig (Idelalisib)  
Zykadia (Ceritinib)  
Zytiga (Abiraterone acetate)  

 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

 

 

 
75 

APPENDIX B: Eastern Cooperative Oncology Group Scale of Performance Status  

& Karnofsky Status Performance Scale 

Table 3.6. Eastern Cooperative Oncology Group Scale of Performance Status & Karnofsky 
Status Performance Scale  

 

 

 

WHO/ECOG 

Grade 

WHO/ECOG Activity 

 
 

 
 

 
 

Fully active, able to carry on 
all normal activities without 
restriction 
Restricted in physically 
strenuous activity but 
ambulatory and able to 
carry out work of a light or 
sedentary nature, e.g., 
light house work, office 
work 

Ambulatory and capable of all 
self-care but unable to carry 
out any work activities. Up and 
about more than 50% of 
waking hours 

 

Capable of only limited self- 
care, confined to bed or chair 
more than 50% of waking 
hours 

Completely disabled. 
Cannot carry on any self-
care, totally confined to bed 
or chair. 

Dead 

 

0 

 

1 

 
 
 
 

 
 
 
 

 
 
 
 

2 

3 

4 

5 

Karnofsky 

Grade 

 

100% 

 
 

90% 

 

80% 

 
 

70% 

 
 

60% 

 

50% 

 

40% 

 
 

30% 

 
 

20% 

 

10% 

0% 

Karnofsky Activity 

Normal no complaints; no 
evidence of disease 
Able to carry on normal activity; minor 
signs or symptoms of disease 

Normal activity with effort; some sign or 
symptoms of disease 

Cares for self; unable to carry on 
normal activity or do active work 

Requires occasional assistance, 
but is able to care for most 
personal needs 
Requires considerable assistance 
and frequent medical care 
Disabled; requires special care and 
assistance 
Severely disabled; hospitalization 
admission is indicated, although 
death not imminent 
Very sick; hospital admission 
necessary; active support 
treatment is necessary 
Moribund; fatal processes 
progressing rapidly 

Dead 

 
Karnofsky DA. The clinical evaluation of chemotherapeutic agents in cancer. Evaluation of Chemotherapeutic Agents. 

1949:191-205. 

Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. 

American journal of clinical oncology. 1982;5(6):649-655. 

 
 
 
 
 
 

 

 
76 

APPENDIX C: Parent Study CONSORT Flow Chart 

 
Figure 3.3. Parent Study CONSORT Flow Chart 

 

 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

 
77 

APPENDIX D: Parent Study Medical Record Audit 

 

 

 
78 

 

 
 
 
 
 
 
 

 

 

 

 

 
79 

 
 
 

 

 

 
80 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 

 

 
81 

 

 
 
 

 

 
82 

 

 
 

 

 
83 

 

 

 

 
84 

 

 

 

 

 
85 

 

 

 

 

 
86 

 

 

 

 
87 

 

 

 

 

 

 
88 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

 

 

 

 
89 

APPENDIX E: Parent Study Screening/Baseline Data Collection Tools 

 
What is your highest level of education completed?  
o No formal education 
o Completed grade school 
o Completed some high school 
o Completed high school 
o Completed some college or technical college or associate degree 
o Completed college 
o Completed graduate/professional degree (post baccalaureate degree)  
o Refused  
 
What is your current marital status?  
o Never married 
o Married 
o Divorced/Separated o Widowed 
o Living together 
o Refused  
 
What is your ethnic background?  
o Hispanic or Latino 
o Not Hispanic or Latino  
o Unknown 
o Refused  
 
What is your race or ethnic background?  
o American Indian or Alaskan Native  
o African American or Black 
o Asian 
o Native Hawaiian or Pacific Islander o White  
o More than one race  
o Unknown 
o Refused  
 
Gender:  
o Male 
o Female  
 
Ethnicity:  
o Hispanic/Latino 
o Not Hispanic/Latino  
 
 
 
 
 
 
 
 
 
 

 

 
90 

Screening Eligibility Form from Parent Study 
(Collecting patient and disease characteristics) 

 

Race (check all that apply): 
o American Indian/Alaska Native  
o Asian 
o Native Hawaiian/Pacific Islander  
o Black/African American 
o White  
 
Cancer Site:  
o Breast 
o Colorectal 
o Gastrointestinal  
o Leukemia 
o Liver 
o Lung 
o Lymphoma  
o Melanoma  
o Myeloma  
o Pancreatic  
o Prostate  
o Renal 
o Sarcoma  
 
Stage:  
o I 
o II 
o III 
o IV 
o Other  
If ‘Other’ write in stage: _______________  
 
On Concurrent IV chemotherapy?:  
o Yes 
o No 
o If yes, medication and frequency:_______________ o  
 
On Concurrent Radiation?  
o Yes 
o No 
o If yes, treatment name and frequency: _______________  
 
Patient Eligibility:  
o Yes o No  
(If NO to ANY of the questions below, patient is NOT eligible)  
 
Can hear on telephone?  
o Yes o No  
 
Can read and understand English?  

 

 
91 

o Yes o No  
 
21 or older?  
o Yes 
o No 
o Age:_______________  
 
ECOG Performance status within 0-2 or Karnofsky performance status within 50-100?  
o Yes 
o No 
o Score:_______________  
 
Has a land line/cell phone with touch pad numbers?  
o Yes o No  
 
Is on an eligible oral cancer medications?  
o Yes o No  
 
Eligibility:  
o Eligible o Ineligible  
 
Enrollment Status:  
o Consented 
o Refused 
o Lost to follow-up  
 
Reason, if refused:  
o Too ill 
o Too busy 
o Lack of interest  
o Other  
 
 
Date Screened: _______________ Recruiter Initials: _______________  
 
 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

 

 

 
92 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

REFERENCES 

 

 
93 

REFERENCES 

 

Bassan F, Peter F, Houbre B, et al. Adherence to oral antineoplastic agents by cancer 
patients: definition and literature review. European Journal of Cancer Care. 
2014;23(1):22-35. 
Hall AE, Paul C, Bryant J, et al. To adhere or not to adhere: Rates and reasons of 
medication adherence in hematological cancer patients. Critical Reviews in 
Oncology/Hematology. 2016;97:247-262. 
Hugtenburg JG, Timmers L, Elders PJ, Vervloet M, van Dijk L. Definitions, variants, and 
causes of nonadherence with medication: a challenge for tailored interventions. Patient 
Preference and Adherence. 2013;7:675-682. 
Mathes T, Antoine SL, Pieper D, Eikermann M. Adherence enhancing interventions for 
oral anticancer agents: a systematic review. Cancer Treat Rev. 2014;40(1):102-108. 
Conti RM, Fein AJ, Bhatta SS. National trends in spending on and use of oral 
oncologics, first quarter 2006 through third quarter 2011. Health Affairs (Project Hope). 
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Zerillo JA, Goldenberg BA, Kotecha RR, Tewari AK, Jacobson JO, Krzyzanowska MK. 
Interventions to Improve Oral Chemotherapy Safety and Quality: A Systematic Review. 
JAMA Oncology. 2018;4(1):105-117. 
Verbrugghe M, Verhaeghe S, Lauwaert K, Beeckman D, Van Hecke A. Determinants 
and associated factors influencing medication adherence and persistence to oral 
anticancer drugs: a systematic review. Cancer Treat Rev. 2013;39(6):610-621. 

Niccolai JL, Roman DL, Julius JM, Nadour RW. Potential Obstacles in the Acquisition of 
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Oncology. 2017;13(1):e29-e36. 
Accordino MK, Hershman DL. Disparities and challenges in adherence to oral 
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American Society of Clinical Oncology Meeting. 2013:271-276. 
Cheung WY. Difficult to swallow: issues affecting optimal adherence to oral anticancer 
agents. American Society of Clinical Oncology Educational Book / ASCO American 
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Geynisman DM, Wong YN. Drug Supply Chain Security Act and Oral Anticancer 
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Dusetzina SB, Huskamp HA, Winn AN, Basch E, Keating NL. Out-of-Pocket and Health 
Care Spending Changes for Patients Using Orally Administered Anticancer Therapy 
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Dusetzina SB, Winn AN, Abel GA, Huskamp HA, Keating NL. Cost sharing and 
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Shen C, Chien CR, Geynisman DM, Smieliauskas F, Shih YC. A review of economic 
impact of targeted oral anticancer medications. Expert Review of Pharmacoeconomics & 
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Use of Targeted Cancer Therapies for the Privately Insured Nonelderly: 2001 to 2011. 
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challenges and opportunities. Journal of Controlled Release: Official Journal of the 
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Sikorskii A, Given CW, Given BA, et al. Do treatment patterns alter beliefs cancer 
patients hold regarding oral oncolytic agents? Psycho-Oncology. 2018;27(3):1005-1012. 
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of Chemotherapeutic Agents. 1949:191-205. 
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Cooperative Oncology Group. American Journal of Clinical Oncology. 1982;5(6):649-
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22. 

 

 

CHAPTER FOUR: THE IMPACT OF BURDEN OF TREATMENT ON TEMPORARY 

STOPPAGES OF TREATMENT IN CANCER PATIENTS WITH MULTIMORBID CONDITIONS 

(Manuscript Three) 

 

Introduction 

It is imperative for oncology providers to understand how the characteristics of an individual can 

affect their cancer and the associated treatment regimen. Age, sex, body mass index (BMI), 

race, and an extensive list of other patient characteristics have been extensively researched 

among cancer patients in order to make oncology providers’ decisions on treatment plan easier. 

However, one patient characteristic that may have a greater influence continues to be a 

challenge for researchers and clinicians. The presence of multimorbid conditions within cancer 

patients brings about unique challenges for providers and patients. As the population continues 

to increase in life expectancy, the issue of cancer and multimorbidity will be an important 

consideration for researchers and clinicians. For patients, the issue of cancer and multimorbidity 

brings about an increased number of tasks needed to manage their diseases, as well as 

distress that accompanies an overwhelming treatment workload.  

 

This combination of a patient workload and their perspective is the burden of treatment. 

The need to examine this concept has become important due to the shift to shorter inpatient 

stays and a greater emphasis on patient self-management in the home.1,2 The increase of 

patient self-management has led to patients being more responsible for their care, and therefore 

a greater workload, or burden, is imposed upon them. Tran, Sav, and Eton are the three primary 

contributors to the burden of treatment conceptual literature. Their work began with the 

conceptualization of burden of treatment and started to empirically measure the concept within 

certain chronic disease populations.3-8  

 

The existing burden of treatment literature has focused on several chronic conditions, 

including HIV/AIDS,9-11 mental illness,15,17 diabetes,12 cardiovascular diseases,13 cystic fibrosis,14 

celiac disease,15 hyperlipidemia,16 asthma,4,16 and stroke,3,16 as well as multimorbidity, or the 

coexisting of more than one chronic condition.1 These chronic condition studies have examined 

 

 
96 

burden of treatment (BOT) through varied designs and variables of interest. Qualitative studies 

identified themes, such as tasks required of patients to manage their conditions; difficulties with 

access to care; impact of tasks on self-management; identifying problem solving techniques and 

coping strategies; importance of social support; and positive aspects of the healthcare system, 

including positive relationships with providers.6,16 Some studies reviewed existing literature to 

examine BOT through conceptual works or by undertaking secondary data analyses. 5,10,11 The 

quantitative studies utilized indicator variables in order to try and examine BOT as an outcome 

variable or predictors of patient and clinical consequences. Indicator variables are used in place 

of the actual variable or concept, due to the fact that it may be more feasible to collect the 

indicator variable rather than a BOT-specific measurement tool. Examples of the indicator 

variables included in these studies are treatment complexity,14,16 number of medications,11,17 

number of interactions with healthcare providers,18-20 and difficulty managing treatment.15-17 

These indicator variables are components of the workload and perspective components of BOT 

but are not all the components. Individuals with chronic diseases are required to complete more 

healthcare tasks than these examples. “Difficultly managing treatment” only provides small 

insight into the patient perspective of individuals with chronic disease.   

 

While these studies examined BOT across a number of chronic conditions, only three 

studies were found that examined BOT within cancer patients. Two of these cancer studies 

analyzed secondary data from large data sets, including the Surveillance Epidemiology and End 

Results (SEER-Medicare)18 and an institutional cancer registry.20 The third study recruited 

patients from a national survey registry and had direct contact with participants through online 

surveys and telephone interviews.19 The three studies examined burden through the following 

indicator variables: the number of days interacting with the healthcare system,18-20 type of 

interaction (i.e. receiving cancer treatment, clinic visit, or ER/acute stay),18-20 number of 

physicians patients were interacting with,18 number of medications,18 and symptom burden.19 

These studies utilize indicator variables identified within the BOT literature as increasing the 

 

 
97 

workload of the patient. Although these studies describe the workload imposed upon patients by 

the healthcare system, the studies do not provide a full picture of cancer patients’ burden 

experience. The studies do not include the patient’s perspective of BOT—how this workload 

increase affects their ability to manage their cancer or the impact on their daily lives.  

While these three studies did use indicator variables for BOT, these works have 

provided valuable insight into the burden imposed upon cancer patients. Looking more 

specifically at cancer and multimorbidity within these studies, only Presley et al.18 collected 

patients’ multimorbid conditions and examined the impact on patients’ burden. The researchers 

used the number of days interacting with the healthcare system as an indicator for BOT. What 

they found across a sample of approximately 8,000 lung cancer patients, when comparing 

patients that received the same cancer treatment, was those with three or more multimorbidities 

experienced a significantly higher level of burden than those with less than three 

multimorbidities. Although this is the only study that specifically examines BOT within cancer 

patients and measures the impact of multimorbid conditions, other works have illustrated the 

impact of multimorbidities on a number of cancer patient and healthcare outcomes, including 

symptoms and side effects, adverse events (e.g. hospitalizations), quality of life, increased 

costs, disease exacerbation, and mortality.2,21-25 These works exemplify the struggles that 

individuals with cancer and multimorbid conditions face on a daily basis, both physically and 

mentally.18-20  

Cancer patients that are taking oral oncolytic agents (OOA) experience a different set of 

challenges when compared to those receiving traditional intravenous (IV) chemotherapy. While 

trained healthcare professionals manage IV chemotherapy administration in a controlled 

environment, oral agents lack such firsthand monitoring, thereby shifting treatment responsibility 

to patients and their families.26-30 Oral oncolytic agent regimens can be quite complex, 

comprising multiple oral medications with varying dosages, cycles (days on and days off), and 

special instructions (e.g., avoid certain medications or food).29,31 These complex regimens 

 

 
98 

increase patients’ BOT.32 Because most cancer and multimorbidity management takes place in 

the home, it is imperative that patients and providers have open communication and providers 

deliver effective education about side effects, as these can lead to dose modifications, 

medication interruptions, a switch to IV agents, or a discontinuation of all cancer treatments.  

A primary concern for oncology providers are the frequent modifications that take place 

with oral oncolytic regimens. These modifications may include dose changes; temporary 

stoppages (i.e. taking some time off from the drug but still continuing with that drug); permanent 

stoppages (i.e. completely discontinuing that drug); and adding or switching to a new cancer 

drug.24,33,34 The regimen modifications can be brought on by side effects and toxicities, 

interactions with other diseases or medications, patient or provider decisions, or progression of 

the cancer.34 Changes in oral oncolytic regimens can alter the effectiveness of the medication, if 

patients are not receiving a strong enough dose or are taking increased rest periods. Although 

these changes may affect the plan to cure the cancer or relieve symptoms, regimen 

modifications may be necessary to keep patients on their oral oncolytics.26,35  

In addition to the increased complexity and modifications of oral oncolytic medication 

regimens, patients may also face financial difficulties from paying for oral agents that can cost 

several thousand dollars per month.36,37 Difficulties with insurance coverage and the need to use 

specialty pharmacies can lead to patients discontinuing or delaying necessary cancer 
treatments.37-40 Dealing with the problems related to oral oncolytic regimens can create a great 

deal of stress, making it difficult for cancer patients to continue treatment. Not being able to pay 

for one’s OOA may even cause a patient to have to stop, temporarily or permanently, their 

oral agent. All of these factors may be multiplied for patients who have other chronic conditions, 

in addition to cancer, that also require complex management. As patients’ workloads increase 

with additional tasks, these challenges can bring about added stress for patients. With limited 

research examining BOT, even with indicator variables, among cancer patients with multimorbid 

conditions, this work will begin to fill that gap.  

 

 
99 

Purpose 

The purpose of this work is to evaluate the moderation effect of BOT indicator variables on the 

relationship between the baseline characteristics (age, sex, employment status, marital status, 

multimorbidity, OOA drug class, insurance type, and OOA copay) and continuation of OOA 

within cancer patients with multimorbid conditions. The following research question will be 

answered: 

•  How the burden of treatment indicator variables might moderate the relationship 

between baseline antecedent factors and the temporary stoppage of patients’ oral 

oncolytic regimens?  

Conceptual Model 

The Burden of Treatment Morbidity (BOTM) Model will guide the conceptualization of this 

chapter, as it has for the other chapters within the dissertation. Figure 4.1 illustrates the an 

analytical model with similar components to the BOTM Model. Unlike the BOTM, this analytical 

model shows a moderation relationship. This moderation model focuses on two primary 

relationships: 1) the direct relationship between the antecedent factors (patient, social, 

disease/treatment, and healthcare system) and the consequences of the patient and healthcare 

system; and 2) how BOT, both patient workload and patient perspective, might moderate the 

strength of the relationship between the baseline antecedent factors and the oral oncolytic 

regimen change. This chapter will focus on the second relationship. This work will utilize 

variables from all four of the antecedent factor categories, an indicator variable for both BOT 

patient workload and BOT patient perspective, and the consequence of treatment regimen 

changes. The arrow from the antecedent through BOT, and the arrow from BOT to the 

consequences, illustrates the strength between the antecedents and consequences is impacted 

by BOT. This analytical model helps the reader visualize the relationships of the variables and 

the moderation analysis that will examine these relationships.  

 

 

100 

 

Figure 4.1. Burden of Treatment Moderation Analysis Model   

 

 

Methods  

This work was a secondary data analysis of a randomized controlled trial (RCT) testing 

adherence and symptom management interventions in cancer patients that have been newly 

prescribe OOAs. The parent study, Improving Adherence to Oral Cancer Agents and Self Care 

of Symptoms Using an IVR, funded by the National Cancer Institute (1Ro1CA162401-O1A1), 

was a two-arm, multisite RCT.41 The experimental arm received a weekly symptom assessment 

with referral to a symptom management toolkit for symptoms ≥4/10 and an adherence 

intervention comprised of daily reminders using an automated interactive voice response (IVR) 

system. Control patients received standard care without referrals to a toolkit or reminder calls. 

Sample 

Parent Study Sample. Participants for the parent study were recruited from six NCI-designated 

comprehensive cancer centers across the Midwest United States. Patients agreed to enroll in 

 

101 

 

the study by signing a consent form, and could withdrawal at any point during the study. 

Inclusion criteria for the parent study were as follows: patients were to be 21 years or older; 

speak English; newly prescribed one of the designated FDA approved oral oncolytics (Appendix 

A); have an Eastern Cooperative Oncology Group (ECOG) performance of 0–2 or Karnofsky 

score of ≥50 (Appendix B);42,43 and actively receiving cancer treatment from one of the 

participating cancer centers. Exclusion criteria for patients included if they had difficulty hearing 

on the telephone; limited or no access to a touch-tone phone; cognitive deficits as determined 

by recruiters; or receiving hospice care.  

  

In the parent study, 482 patients were screened for eligibility, with 438 being eligible, and 

322 of these patients consenting for the study. Of the consented patients, 272 patients 

completed the baseline interview and then randomized into one of the two arms (Appendix C). 

Institutional review boards (IRB) at the study center and each recruitment site approved the 

study and all protocols.  

Data Collection. Data were collected over the phone by trained interviewers at baseline, 

4, 8, and 12 weeks. Interviewers collected information including OOA prescription information 

and pill counts, demographics, insurance coverage, social support, symptoms, multimorbid 

conditions, and interactions with healthcare providers. Data were also collected during the 

experimental group’s daily reminder calls, as well as all of the patients’ automated weekly, 

symptom calls. After patients completed the study, trained abstractors at the respective cancer 

centers completed medical record audits (MRA) (Appendix D). Every MRA received quality 

assurance checks, as well as randomly selected interviews each month for all patient 

interviewers.  

Secondary Data Sample. All 272 participants that completed the baseline interview will 

be included in this secondary data analysis. Participants are not differentiated by 

experimental/control groups as the interventions had no impact over and beyond usual care.  

 

 

102 

 

Measures 

Table 4.1. summarizes the constructs, measures, and descriptions from the parent study 

utilized in this secondary analysis. Patient sociodemographics include age, sex, marital status, 

and employment status (Appendix E). Healthcare system antecedents include the type of 

insurance and presence of an out of pocket (OOP) cost for the oral oncolytic at baseline. Oral 

oncolytic drug class and multimorbidities requiring medication management are used for 

disease/treatment antecedents. These multimorbidities were based on pre-existing (before the 

study period) prescriptions for medications primarily used to treat the specific conditions, found 

in the medical record. Two nurses on the research team reviewed the multimorbid conditions 

and medications.  

Table 4.1. Secondary Data Analysis BOT Moderation Variables 

Construct 

Measures 

Description 

Patient 
Characteristics 

Age, Sex, Employment 
Status, Marital Status, 

Cancer 
Characteristics 
Healthcare 
System 

 
Burden of 
Treatment 

Multimorbid Conditions 

Treatment Protocol 

Insurance Type 
Oral Oncolytic Out of Pocket 
(OOP) Cost 
Medication Regimen 
Complexity Index (MRCI)31 
Cancer Symptom Experience 
Inventory (CSEI) – 
Interference Scale 

Outcome 
Variable 

Temporary Stoppage 

 

 

 

Patient reported during screening and baseline interview 
Age (continuous); Sex (male, female); Employment Status 
(employed, unemployed); marital status (married, 
divorced/widowed, never married) 
Pre- existing prescriptions reported in MRA used for 
management of conditions. Multimorbidities will be 
analyzed as a continuous variable 
Class of the oral oncolytic (cytotoxic, kinase inhibitor, sex 
hormone inhibitor, and other) 

2 categories: government and private/commercial 
Yes/No. Patient had OOP cost for their oral oncolytic at 
baseline 
Weighted algorithm of 19 variables in order to capture the 
complexity of the oral oncolytic protocol  
Patient reported. Summed score of 19 variables with scale 
from 0-9 on the interference on daily activities (range 0- 
162) 
Whether or not patient experienced temporary stoppage of 
their OOA regimen 

Burden of treatment indicator moderation variables are being used for both patient 

workload and patient perspective. Patient workload was captured through oral oncolytic regimen 

complexity scores, which were measured using an adapted version of George’s et al. 

 

103 

 

Medication Regimen Complexity Index (MRCI).44 George et al. created the complexity index in 

order to capture the complexity of a patient’s medication regimen, including times per day, 

having to crush or dissolve pills, having to take with specific food or liquid, and taking multiple 

pills per dose. The pharmacy team designated weights for specific variables, with larger weights 

designated variables that add more complexity to the medication regimen. George and 

colleagues worked through multiple levels of measurement testing in order to ensure the most 

valid and reliable tool. Expert panels tested actual patient medication regimens, who had a 

primary diagnosis of chronic obstructive pulmonary disease (COPD) but also taking medications 

for other conditions. Six different levels of evaluation took place involving the authors, 

pharmacists, a research nurse, an adherence expert, a pharmacy academic, and a home-

medication-review consultant. Each step included different evaluations, such as content validity, 

inter-rater reliability, test-retest reliability, and discussions to modify the tool for user feasibility, 

proper weightings, and completeness of included variables. Kendall’s coefficient of concordance 

was used to correlate the agreement on patient medication regimens between five experts. 

Spearman’s Rho was used to determine correlation between the experts and the MRCI. 

Significant agreement was found between the expert panel and the new MRCI tool when 

ranking the patient medication regimens 

The parent trial study applied 18 specific variables of the MRCI to the patients’ regimens 

in order to capture the complexity of their oncolytic regimen. The others variables did not apply 

to oral oncolytic regimens and were excluded. The only other added variables were each 1-point 

weighted variables and were for the number of injections and IV chemotherapies in patients’ 

baseline treatment protocols. Table 4.2. shows all of the included variables from the MRCI with 

a scoring example for a possible patient regimen. Below is a detailed example. 

A breast cancer patient prescribed Xeloda (capecitabine) is also receiving platinum-

based IV chemotherapy and a monthly adjuvant hormonal injection. The Xeloda is 500mg tabs 

with four pills in the morning and three pills in the evening, cycling the medication two weeks on 

 

104 

 

and one week off. The number of oral oncolytic medications that fit the variable criteria is added 

to that box. For this patient, the number of variables that match the Xeloda regimen is “1.” 

Patients on multiple medications might have a two or more if multiple medications matched the 

variable criteria. After entering the total number of medications that match each variable, these 

numbers are multiplied by the variable weighting. The weighted variable scores are added 

together for a final patient complexity score. This example patient will result in a complexity 

score of 12. 

Table 4.2. Medication Regimen Complexity Index (MRCI) Adaptation Description 

Variable 

Description 

Weighting of 

Variable 

Patient 
Example 

1. Patient ID 
2. Start Date 

3. End Date 

4. Capsule/Tablets 
5. Once Daily 
6. Once Daily PRN 
7. Twice Daily 
8. Twice Daily PRN 

9. Three Times Daily 
10. Three Times Daily 
PRN 

11. Cycling 

12. Break or Crush 

13. Dissolve 

14. Multiple Pills per 
Dose 

15. Variable Dose 

16. Take at a Specific 
Time 

17. Relation to Food 

 

Patient ID 
Date for which the oral agent 
chemotherapy (OA) protocol began 
Date for which the OA protocol 
stopped or changed 
Number of all individual oral 
medications in OA Protocol  
Medication taken once per day 
Medication taken once per day as 
needed 
Medication taken twice per day 
Medication taken twice per day as 
needed 
Medication taken three times per 
day 
Medication taken three times per 
day as needed 
If medication is not taken every day, 
but instead a cycle format. There will 
be certain days to take the 
medication and certain days to not 
Medication must be broken or 
crushed before administering 
Medication must be dissolved in 
liquid, usually water, before 
administering 
More than one pill to be taken with 
each dose 
Not a specific number of pills per 
dose, but for 
example could be 2-3 pills per dose 
Medication must be administered at 
a specific time of the time (e.g. 9 
a.m. and 7 p.m.) 
Specific directions, such as to be 
taken on an empty stomach or with 
food 

105 

 

N/A 
N/A 

N/A 

1 
1 
0.5 
2 
1 

3 

1.5 

2 

1 

1 

1 

1 

1 

1 

 
 

 

1 
0 
0 
1 
0 

0 

0 

1 

0 

0 

1 

1 

0 

1 

Weighted 
Patient 
Example 

 
 

 

1 
0 
0 
2 
0 

0 

0 

2 

0 

0 

1 

1 

0 

1 

Table 4.2. (cont’d) 

 

Variable 

Description 

 

 

Weighting of 

Variable 

Patient 
Example 

 

Weighted 
Patient 
Example 

18. Take with Specific 
Liquid 

19.Tapering/Increasing 
Dose 

20. Alternating Dose 

21. Injection Therapy 

Specific directions in relation to 
liquid, such as take with a full glass 
of water or drink 2L per day 
If the original prescription starts at a 
specific dose and then after a 
certain amount of time (i.e. 2 weeks) 
increase or decrease the dosage by 
a specified amount 
Different doses during the day (e.g. 
take 2 pills in the morning and 3 pills 
at night) 
Point for each injection therapy on 
the OA 
protocol 
Point for each IV therapy on the OA 
protocol 
Summed score of variables 4-22, 
with weighting taken into account 

1 

2 

2 

1 

0 

0 

1 

1 

0 

0 

2 

1 

1 

1 

22. Intravenous (IV) 
Therapy 
23. Complexity Score 
Adapted Version of George et al. Medication Regimen Complexity Index (MRCI). Weighting was determined by the 
George’s team. Only the above variables were selected, as they apply to oral oncolytics, while the others did not. An 
algorithm was created by this researcher to multiply the number of medications in each filed by its respective weight, 
and then add each variable to equal the complexity score. 
 

N/A 

12 

1 

 

The indicator BOT-patient perspective variable is patient baseline-symptom interference. 

Cancer Symptom Experience Inventory (CSEI) measured symptom interference, which contains 

reports on 18 symptoms commonly associated with oral oncolytic therapy (Appendix F).45 

Patients were asked if they have had each symptom in the last seven days due to cancer or its 

treatment. If they said “yes,” patients rated the symptom severity at its worst on a 1–9 scale (1 = 

very little; 9 = worst possible) and rated overall interference in daily activities over the last seven 

days on a scale from 0–9 (0 = did not interfere; 9 = interfered completely). Interference is 

associated with patient perspective of the burden brought on by their cancer and the treatment. 

The “0–9” interference scores for each of the 18 symptoms will be summed, with a higher score 

meaning greater interference from the cancer or its treatment on the patient’s daily activities 

(range 0–162). Symptom interference was included over symptom severity, because it better 

captures patients’ perspective of how symptoms impact their lives.   

 

106 

 

The outcome variable is a binary variable of whether or not patients experienced a 

temporary stoppage of their OOA regimen over the course of the parent trial (12 weeks). Oral 

oncolytic agent regimens may require a number of changes, including dose changes, temporary 

stoppages, and permanent stoppages. Patients have more of a chance to influence temporary 

stoppages than permanent stoppages, either through effective management of symptoms and 

side effects or decisions made by themselves or their oncologist. Temporary stoppages are 

often accompanied by dose reductions, and dose reductions are often less burdensome to 

patients. Permanent stoppages are most often due to disease progression and therefore, have 

little to no patient input.  

Data Analysis 

Data were analyzed using STATA/IC 14.0 Software. Descriptive statistics and 

frequencies were used to measure patient and cancer characteristics and the frequency of 

temporary cancer treatment regimen stoppages. Effect coding was used for the categorical 

predictor variables within the models, coded as (1, -1). In order to standardize continuous 

variables, the group means for regimen complexity, symptom interference, and number of 

multimorbid conditions were subtracted from each individuals’ value.  

For the primary research question, a moderation analysis was implemented using two 

multiple logistic regression models, based on Baron and Kenny and MacKinnon.46 The outcome 

for both models was whether an individual experienced a temporary stoppage during the 12-

week parent trial. Because the primary focus for this dissertation is the burden of cancer 

patients with multimorbid conditions, patient multimorbidities will be the antecedent variable 

included in the interaction term with the BOT indicator variables (regimen complexity and 

symptom interference). Chi-square tests and simple logistic regressions were completed for 

categorical and continuous variables, respectively, to test the main effect of each predictor on 

the outcome of temporary stoppages. Significant main effects and the moderation variables 

were included in the final regression models. If the moderation interaction terms were found to 

 

107 

 

be non-significant, then a main effects regression model including the significant main effects 

and three moderation variables (multimorbidity, regimen complexity, and symptom interference) 

would be analyzed.  

Results 

The secondary analysis sample included all 272 participants from the parent trial that completed 

the baseline interview. The sample was evenly split between males and females and had a 

mean age of 61 years (Table 4.3.). Approximately one third of the sample was employed, either 

part time or full time, and 61% were married at the time of their intake interview. More than half 

of patients’ (57%) primary insurance was government coverage, which included Medicare, 

Medicaid, and Veterans Affairs (VA). For their initial OOA prescription, 53% had an OOP cost, 

with a mean cost of $258 (USD). Of the 28 different main oral agents prescribed to patients, 

82% were either in the cytotoxic or kinase inhibitor drug-classes. In addition to being prescribed 

an OOA, 27% of the trial sample were also receiving IV chemotherapy at baseline. Table 4.4. 

displays the sex differences of the continuous predictor variables, with females having a 

significantly higher mean symptom interference score (P 0.039).  

Table 4.5. displays the correlations between the continuous predictor variables. Age was 

significantly correlated with multimorbid conditions (P < .01) and symptom interference (P = 

.01). The number of multimorbid conditions increased with age. The opposite was true for 

symptom interference; as age decreased, symptom interference increased. Both of these 

relationship are well documented Both of these relationship are well documented within 

literature. As individuals age they have a greater likelihood to be diagnosed with chronic 

conditions.2 Younger cancer patients often have a more aggressive disease status, therefore, 

having a higher symptom burden.47 

 

In addition to their cancer, patients had, on average, 3.39 chronic conditions requiring 

medication management, with an average of 12 non-cancer treatment medications per patient. 

The most common multimorbidities were cardiovascular disease, peptic ulcer disease, 

 

108 

 

depression, diabetes, hyperlipidemia, and renal disease. A patient’s average treatment-regimen 

complexity score was 6.8 (range: 1–16). Patients had an average baseline symptom-

interference score of 17.8 (range: 0–119). Over the course of the 12-week parent trial, 99 

patients (36%) experienced at least one temporary stoppage of their cancer treatment regimen. 

Table 4.6. shows the frequency of temporary interruptions by the baseline categorical variables. 

 

In building the final moderation regression models, chi-square tests showed sex and 

OOA drug-class had significant group differences in terms of temporary stoppages. Females 

were more likely to experience a temporary stoppage over the course of the trial (P < 0.001). 

After testing OOA drug-class using logistic regression with effect coding, those prescribed 

cytotoxics (P = 0.049) and kinase inhibitors (P = 0.004) experience a greater number of 

temporary stoppages than those taking sex hormone inhibitors and other OOAs. Age, marital 

status, employment status, type of insurance, and whether or not patients had an OOP cost for 

their first OOA prescription showed no significant differences in terms of temporary stoppages, 

and thus were not included in the final moderation regression models. Given that age was non-

significant, the issue of multicollinearity with multimorbid conditions and symptom interference is 

not a problem moving forward with the multiple regression models.  

 

Table 4.7. shows the results for the first moderation regression model that included the 

interaction term of multimorbidity and cancer treatment-regimen complexity (patient workload). 

Similar to the individual model building tests, both sex and OOA drug class had significant 

effects on temporary stoppages. However, when including all of the predictor variables in the 

model, cytotoxics no longer had a significant effect. Females (P 0.018) and those prescribed 

kinase inhibitors (P 0.003) were more likely to experience temporary stoppages. Neither the 

main effects of multimorbidity and treatment regimen complexity, nor the interaction term 

between the two variables had a significant impact on temporary stoppages over the course of 

12 weeks. However, cytotoxic agents no longer has a significant impact.  

 

109 

 

 

Table 4.8. shows the results for the moderation regression models that include the 

interaction between multimorbidity and baseline symptom interference (patient perspective). 

This model had similar results to the previous model that included multimorbidity and regimen 

complexity. Females (P = 0.008) and those prescribed kinase inhibitors (P = 0.01) were more 

likely to experience a temporary stoppage over the course of the trial. Neither the main effects 

of multimorbidity and symptom interference, nor the interaction between multimorbidity and 

symptom interference had a significant impact on temporary stoppages of a patient’s cancer 

treatment regimen. As was the case in patient workload X multimorbidity model, cytotoxic 

agents no longer had a significant impact.  

Both of the interaction terms between symptom interference and multimorbidity and 

OOA regimen complexity and multimorbidity were non-significant within their respective models. 

Therefore, a regression model was conducted that included sex, OOA drug class, symptom 

interference, regimen complexity, and multimorbidity (Table 4.9.). Much like the interaction 

terms, the main effects for the BOT variables were non-significant. However, like the moderation 

models, sex and OOA drug class were significant. Females (P=0.018) and patients prescribed 

kinase inhibitors (P=0.004) were more likely to experience a temporary stoppage during the 

parent trial. Much like the moderation models, cytotoxic agents no longer had a significant 

impact.  

Discussion 

Individuals with cancer are continuing to live longer as cancer treatments have become more 

effective. Consequently, this longer survival time increases the chances that an individual with 

cancer will have other chronic conditions that require medical management. With the population 

of individuals with cancer and multimorbid conditions rising, it is important to understand the 

BOT these individuals face while managing their cancer and multimorbid conditions.2,22 This 

work is the first study that could be found to examine the BOT of individuals prescribed OOAs 

 

110 

 

that have to medically manage other multimorbid conditions. Similar to previous cancer BOT 

studies,18-20 this work used indicator variables instead of a BOT-specific measure in order to 

measure BOT components. However, unlike these three previous studies, this analysis included 

an indicator variable for patient perspective (patient reported symptom interference) as well as 

an indicator variable for patient workload (cancer treatment regimen complexity). 

 

Oral oncolytic agents are a last line of treatment for most cancer patients, and patients 

could theoretically be taking them until the end of life.21,48 Symptom burden is a primary concern 

for oncology providers in the management of OOAs. Increased symptom burden is one of the 

main reasons for patients needing temporary stoppages or dose changes with their OOA 

regimens. Symptoms were the most common reason for temporary stoppages in the parent trial. 

Of the 99 participants that experienced a temporary stoppage, the most common symptoms and 

toxicities associated with temporary stoppages were fatigue, anemia, abnormal blood counts, 

cold/flu-like symptoms, nausea/vomiting, skin rash, and hand-foot syndrome. Additionally, about 

25% of the sample experienced a permanent stoppage of their treatment regimen. Permanent 

stoppages are primarily driven by disease progression, and are therefore not as influenced by 

the patient like temporary stoppages.49 

 

The results of the initial group difference analysis indicate females and those prescribed 

kinase inhibitors and cytotoxic agents were more likely to experience a temporary stoppage of 

their cancer treatment regimen. The two moderation regression models showed similar results. 

However, when factoring in sex, multimorbidity, treatment regimen complexity, and baseline 

symptom interference, cytotoxic agents no longer had a significant difference in terms of 

temporary stoppages. Given the different symptom profiles associated with certain OOAs, 

differences in temporary stoppages among OOA drug classes was expected.50,51 The most 

commonly prescribed kinase inhibitors in the parent trial were palbociclib, pazopanib, sorafenib, 

and regorafenib.  

 

111 

 

To explore the significant impact of sex on temporary interruption, an additional analysis 

was completed examining the differences between males and females. Although females had a 

significantly higher mean symptom interference score at baseline (P = 0.038), there was no 

difference between males and females after taking their OOAs for four weeks. With symptoms 

being the primary driver behind temporary stoppages, it is unclear why females experienced a 

significantly higher number of temporary interruptions than males in the parent trial. This could 

possibly be related to females having a slightly higher average OOA complexity regimen score 

and more multimorbid conditions than males. It could also be related to females taking OOAs 

that had higher proportions of temporary stoppages. Future research will be needed to examine 

sex differences related to OOA regimen modifications.    

The results of the moderation regression analyses showed no significant impact on 

temporary stoppages within the two models resulting from the main effects of cancer treatment-

regimen complexity (patient workload), baseline symptom interference (patient perspective), or 

multimorbidity and the significant interaction effect. This was not an expected finding as recent 

literature has described the impact of multimorbidities on the management of cancer treatment 

and overall quality of life of cancer patients.2,22,52-54 Although multimorbidity was non-significant, 

there was a descriptive trend of increased multimorbid conditions being associated with a larger 

proportion of temporary stoppages. The reason for lack of significant findings could be related to 

the measurement of multimorbidities, which was operationalized based on non-cancer 

prescription medications within patients’ MRA. The same could be said for the use of BOT-

indicator variables as opposed to a BOT-specific measure. Although not statistically significant, 

individuals with multimorbid conditions had a higher frequency of temporary stoppages than 

patients with no multimorbidities. Given this, more research is needed examining the impact of 

multimorbid conditions on the treatment outcomes of patients prescribed OOAs.   

 

 

 

112 

 

Limitations 

Utilizing indicator variables for the BOT components was a limitation of this secondary analysis. 

Utilizing other measures for patient workload and perspective, such as a validated BOT scale, 

may have yielded different results within the study sample. Inclusion of ICD-10 diagnoses within 

the parent trial may have improved the measurement of multimorbidities. The sample lacked 

racial diversity and was highly educated, and thus was not representative of the U.S. population. 

Finally, the follow-up period of the parent trial was 12 weeks. A longer follow-up time may have 

shown different results as patients’ cancer treatments progress. 

Conclusions 

In the secondary analysis, we found that more than a third of patients experienced a temporary 

stoppage in their cancer treatment regimen over the course of 12 weeks. With a short 

observation period, this shows the volatility of cancer treatment for those prescribed OOAs. 

Females and patients prescribed kinase inhibitors were more likely to experience temporary 

stoppages than males and patients prescribed other OOA class, respectively. These patients 

are often in a difficult period of their cancer care trajectory, as the majority of solid tumor cancer 

patients have gone through several lines of treatment before being prescribed OOAs. If these 

patients medically manage other chronic conditions as well, the complexity of management and 

level of burden may be increased. This work begins to highlight the importance of examining 

factors that may affect patients’ OOA regimens. Although multimorbidity did not prove to have a 

significant impact within this sample, the impact of multimorbid conditions on the care and 

quality of life of cancer patients needs further research. As the population lives longer, 

individuals with cancer and other multimorbid conditions will become a norm. Continued 

research efforts within this population will be vital for practice guidelines and empowering 

patients to self-manage their OOAs and other multimorbid conditions in the home effectively.  

 
 
 

 

113 

 

Table 4.3. Descriptive Statistics for the Secondary Analysis Study Sample (N=272) 
Characteristic 
Sex 

n (%)               

             22 (8) 
241 (89) 
                                                 7 (3) 

 
136 (50) 
       136 (50) 
 

 
71 (26) 
150 (55) 
49 (18) 
2 (1) 

 
88 (32) 
   184 (68) 
 
167 (61) 
   105 (39) 
 
154 (57) 
118 (43) 
 
143 (53) 
114 (42) 
15 (5) 

114 

 

Male  
Female 

Race 

African American  
Caucasian  
Other/unknown 

Level of education 
High school or less 
Some college or completed college 
Graduate or professional degree 
Unknown 

Employment status 
Employed 
Unemployed 
Marital status 

Married 
Not married 

Insurance type 

Government 
Private/Commercial 

Oral agent copay 

Yes 
No 
Don’t know 

Drug category 

Yes 
No 

Yes 
No 

Age (years) 
Multimorbid Conditions 
Cancer Treatment Regimen 
Complexity 
Baseline Symptom Interference 

 
 
 
 
 
 
 
 
 

 

Cytotoxic agents 
Kinase inhibitors 
Sex hormone inhibitors 
Other 

IV Chemotherapy at Baseline 

Temporary Treatment Interruption 

 
95 (35) 
127 (47) 
27 (10) 
                                               23 (8)    
 
73 (27) 
199 (73) 
 
99 (36) 
173 (64) 

 
 
Table 4.4. Sex Differences of Predictor Variables (N=272) 
Characteristic 

Male 

Mean (SD) 

63.10 (12.34) 
3.22 (2.04) 
6.67 (2.71) 
15.33 (20.36) 

Female 

Mean (SD) 
59.67 (11.89) 
3.55 (1.93) 
6.95 (2.62) 
20.35 (19.55) 

Total 

Mean (SD) 
61.38 (12.22) 
3.39 (1.99) 
6.81 (2.67) 
17.84 (20.08) 

Table 4.5. Correlations of Continuous Predictor Variables 
 

 

Age  Multi 

SI 

CRC 

Age 
Multi 
SI 
CRC 

 1.0000 
 0.1825* 
-0.1548* 
-0.0457 

 
 1.000 
 0.0086 
-0.0145 

 
 
 

 
 
 1.000 
 0.011  1.000 

Abbreviations: Multi, multimorbidity; SI, symptom interference; CRC, cancer regimen complexity 
*Statistically significant correlation 
 
 
 
Table 4.6. Frequency of Temporary Interruptions by Baseline Antecedent Factors (N=272) 

 

Baseline  

Characteristic 

Temporary Interruption 
Yes 
No 

        n (%) 

      n (%) 

Total  

 

 

 
136  
136  
 
157 
115 
 
167 
105 
 
88 
184 
 
21 
127 
124 
 
95 
127 
23 
27 

 
154 
118 
 
143 
114 
15 
272 

Sex 

Age 

Male 
Female  

<65 Years Old 
65+ Years Old 

Marital Status 

Married  
Not Married 

Employment Status 

Employed  
Not Employed 

No. of Multimorbidities 

0 
1-3 
4+ 

Drug Class  
Cytotoxic 
Kinase Inhibitors 
Others 
Sex Hormone 
Inhibitors  

Insurance Type 
Government 
Private 

OOA Copay 

Yes 
No 
Not sure 

Total 

 
 
 
 

 

 
34 (25%) 
65 (48%) 
 
57 (36%) 
42 (37%)  
 
60 (36%) 
39 (37%) 
 
33 (38%) 
66 (36%) 
 
4 (19%) 
46 (36%) 
49 (40%) 
 
35 (37%) 
55 (43%) 
8 (35%) 
1 (4%) 

 
102 (75%) 
71 (52%) 
 
100 (64%) 
73 (63%) 
 
107 (64%) 
66 (63%) 
 
55 (62%) 
118 (64%) 
 
17 (81%) 
81 (64%) 
75 (60%) 
 
60 (63%) 
72 (57%) 
15 (65%) 
26 (96%) 

 
61 (40%) 
38 (32%) 
 
51 (36%) 
41 (36%) 
7 (47%) 
99 (36%) 

 
93 (60%) 
80 (68%) 
 
92 (64%) 
73 (64%) 
8 (53%) 
173 (64%) 

115 

 

Table 4.7. Multimorbidity x Regimen Complexity Moderation Effect on Temporary Stoppages  
 
Sex 

p-value 

B (SE) 

OR 

Male 
Female (ref) 

Drug Class  
Cytotoxic 
Kinase Inhibitors 
Others 
Sex Hormone Inhibitors 

 
-0.332 (0.14) 

 
0.563 (0.34) 
1.053 (0.35) 
0.549 (0.45) 

0.049 (0.07) 
0.094 (0.06) 
0.044 (0.03) 

(ref) 
Multimorbidity 
Regimen Complexity 
Multimorbidity x Regimen 
Complexity 
Abbreviations: SE, standard error; OR, odds ratio; ref, referent 
 
Table 4.8. Multimorbidity x Symptom Interference Moderation Effect on Temporary Stoppages 
 
Sex 

1.051 
1.099 
1.045 

p-value 

B (SE) 

0.472 
0.118 
0.117 

OR 

Male 
Female (ref) 

Drug Class  
Cytotoxic 
Kinase Inhibitors 
Others 
Sex Hormone Inhibitors 

 
-0.366 (0.14) 

 
0.557 (0.33) 
0.811 (0.32) 
0.375 (0.43) 
 

0.027 (0.07) 
0.002 (0.01) 
0.001 (0.00) 

(ref) 
Multimorbidity 
Symptom Interference 
Multimorbidity x Symptom 
Interference 
Abbreviations: SE, standard error; OR, odds ratio; ref, referent 
 
Table 4.9. Predictor Variable Main Effects on Temporary Stoppages 
 
Sex 

B (SE) 

OR 

Male 
Female (ref) 

Drug Class  
Cytotoxic 
Kinase Inhibitors 
Others 
Sex Hormone Inhibitors 

 
-0.329 (0.14) 

 
0.495 (0.33) 
0.980 (0.34) 
0.456 (0.44) 
 

(ref) 
Multimorbidity 
Symptom Interference 
Regimen Complexity 
Abbreviations: SE, standard error; OR, odds ratio; ref, referent 

0.035 (0.07) 
0.003 (0.01) 
0.090 (0.06) 

1.036 
1.003 
1.094 

 

116 

 

 
0.717 

 
1.755 
2.867 
1.731 
 

 
0.694 

 
1.746 
2.250 
1.455 

1.027 
1.003 
1.001 

 
0.719 

 
1.641 
2.667 
1.578 

 
0.018 

 
0.095 
0.003 
0.220 

 
0.008 

p-value 

 
0.090 
0.010 
0.384 

0.690 
0.658 
0.824 

 
0.018 

 
0.135 
0.004 
0.296 

0.604 
0.695 
0.125 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

APPENDICES 

 

117 

 

 

 

APPENDIX A: FDA Approved Oral Oncolytic Agents Included in the Parent Study 

 
Table 4.10. FDA Approved Oral Oncolytic Agents Included in the Parent Study 

Oral Oncolytic Agents Organized Alphabetically by Trade Name 

Afinitor (Everolimus)  
Bosulif (Bosutinib)  
Gilotrif (Afatinib)  
Gleevec (Imatinib)  
Ibrance (Palbociclib)  
Imbruvica (Ibrutinib)  
Inlyta (Axitinib)  
Lenvima (Lenvatinib)  
Lonsurf (Tipiracil & Trifluridine)  
Lynparza (Olaparib)  
Nexavar (Sorafenib)  
Pomalyst (Pomalidomide)  
Revlimid (Lenalidomide)  
Sprycel (Dasatinib)  

Stivarga (Regorafenib)  
Sutent (Sunitinib)  
Tafinlar (Dabrafenib)  
Tarceva (Erlotinib)  
Tasigna (Nilotinib)  
Temodar (Temozolomide)  
Tykerb (Lapatinib)  
Votrient (Pazopanib)  
Xalkori (Crizotinib)  
Xeloda (Capecitabine)  
Xtandi (Enzalutamide)  
Zydelig (Idelalisib)  
Zykadia (Ceritinib)  
Zytiga (Abiraterone acetate)  

 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

 

 

118 

 

APPENDIX B: Eastern Cooperative Oncology Group Scale of Performance Status  

& Karnofsky Status Performance Scale 

 

Table 4.11. Eastern Cooperative Oncology Group Scale of Performance Status & Karnofsky 
Status Performance Scale  

 

 

WHO/ECOG 

Grade 

WHO/ECOG Activity 

 
 

 
 

 
 

Fully active, able to carry on 
all normal activities without 
restriction 
Restricted in physically 
strenuous activity but 
ambulatory and able to 
carry out work of a light or 
sedentary nature, e.g., 
light house work, office 
work 

Ambulatory and capable of all 
self-care but unable to carry 
out any work activities. Up and 
about more than 50% of 
waking hours 

 

Capable of only limited self- 
care, confined to bed or chair 
more than 50% of waking 
hours 

Completely disabled. 
Cannot carry on any self-
care, totally confined to bed 
or chair. 

Dead 

 

0 

 

1 

 
 
 
 

 
 
 
 

 
 
 
 

2 

3 

4 

5 

Karnofsky 

Grade 

 

100% 

 
 

90% 

 

80% 

 
 

70% 

 
 

60% 

 

50% 

 

40% 

 
 

30% 

 
 

20% 

 

10% 

0% 

Karnofsky Activity 

Normal no complaints; no 
evidence of disease 
Able to carry on normal activity; minor 
signs or symptoms of disease 

Normal activity with effort; some sign or 
symptoms of disease 

Cares for self; unable to carry on 
normal activity or do active work 

Requires occasional assistance, 
but is able to care for most 
personal needs 
Requires considerable assistance 
and frequent medical care 
Disabled; requires special care and 
assistance 
Severely disabled; hospitalization 
admission is indicated, although 
death not imminent 
Very sick; hospital admission 
necessary; active support 
treatment is necessary 
Moribund; fatal processes 
progressing rapidly 

Dead 

 
Karnofsky DA. The clinical evaluation of chemotherapeutic agents in cancer. Evaluation of Chemotherapeutic Agents. 

1949:191-205. 

Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. 

American journal of clinical oncology. 1982;5(6):649-655. 

 
 
 
 
 
 

 

119 

 

APPENDIX C: Parent Study CONSORT Flow Chart 

 
Figure 4.2. Parent Study CONSORT Flow Chart 

 

 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

120 

 

 

 
 
 
 
 
 
 
 

 

APPENDIX D: Parent Study Medical Record Audit 

 

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Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

 

 

 

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APPENDIX E: Parent Study Screening/Baseline Data Collection Tools 

 
What is your highest level of education completed?  
o No formal education 
o Completed grade school 
o Completed some high school 
o Completed high school 
o Completed some college or technical college or associate degree 
o Completed college 
o Completed graduate/professional degree (post baccalaureate degree)  
o Refused  
 
What is your current marital status?  
o Never married 
o Married 
o Divorced/Separated o Widowed 
o Living together 
o Refused  
 
What is your ethnic background?  
o Hispanic or Latino 
o Not Hispanic or Latino  
o Unknown 
o Refused  
 
What is your race or ethnic background?  
o American Indian or Alaskan Native  
o African American or Black 
o Asian 
o Native Hawaiian or Pacific Islander o White  
o More than one race  
o Unknown 
o Refused  
 
Gender:  
o Male 
o Female  
 
Ethnicity:  
o Hispanic/Latino 
o Not Hispanic/Latino  
 
 
 
 
 
 
 
 
 
 

 

133 

 

Screening Eligibility Form from Parent Study 
(Collecting patient and disease characteristics) 

 

Race (check all that apply): 
o American Indian/Alaska Native  
o Asian 
o Native Hawaiian/Pacific Islander  
o Black/African American 
o White  
 
Cancer Site:  
o Breast 
o Colorectal 
o Gastrointestinal  
o Leukemia 
o Liver 
o Lung 
o Lymphoma  
o Melanoma  
o Myeloma  
o Pancreatic  
o Prostate  
o Renal 
o Sarcoma  
 
Stage:  
o I 
o II 
o III 
o IV 
o Other  
If ‘Other’ write in stage: _______________  
 
On Concurrent IV chemotherapy?:  
o Yes 
o No 
o If yes, medication and frequency:_______________ o  
 
On Concurrent Radiation?  
o Yes 
o No 
o If yes, treatment name and frequency: _______________  
 
Patient Eligibility:  
o Yes o No  
(If NO to ANY of the questions below, patient is NOT eligible)  
 
Can hear on telephone?  
o Yes o No  
 
Can read and understand English?  

 

134 

 

o Yes o No  
 
21 or older?  
o Yes 
o No 
o Age:_______________  
 
ECOG Performance status within 0-2 or Karnofsky performance status within 50-100?  
o Yes 
o No 
o Score:_______________  
 
Has a land line/cell phone with touch pad numbers?  
o Yes o No  
 
Is on an eligible oral cancer medications?  
o Yes o No  
 
Eligibility:  
o Eligible o Ineligible  
 
Enrollment Status:  
o Consented 
o Refused 
o Lost to follow-up  
 
Reason, if refused:  
o Too ill 
o Too busy 
o Lack of interest  
o Other  
 
 
Date Screened: _______________ Recruiter Initials: _______________  
 
 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 
 

 

 

135 

 

APPENDIX F: Cancer Symptom Experience Inventory 

 

 

 

 

 

136 

 

 

 
 

 

 

137 

 

 

 

 

 

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139 

 

 

 

 

140 

 

 

 

Given, B.A., Given, C.W., & Sikorskii (2013–2017). Improving adherence to oral cancer agents and self care of 

symptoms using an IVR (1R01CA162401-O1A1). [National Cancer Institute clinical trial]. Retrieved from 
https://clinicaltrials.gov/ct2/show/NCT02043184.  

 

 

 

141 

 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

REFERENCES 

 

142 

 

 

 

 

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Hall AE, Paul C, Bryant J, et al. To adhere or not to adhere: Rates and reasons of 
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CHAPTER FIVE: CONCLUSIONS 

 

Introduction   

The purpose of this dissertation was to examine the concept of burden of treatment (BOT) 

within cancer patients with multimorbid conditions. This examination was done both 

conceptually and operationally. Three manuscripts were developed in order to address the gaps 

in the current BOT and cancer literature. The first manuscript was a concept analysis that 

examined burden of treatment specifically within cancer patients with multimorbid conditions. A 

conceptual model was developed to examine the relationship between the antecedents, 

attributes, and consequences of the concept of BOT within cancer patients with multimorbid 

conditions.  

The final two manuscripts were secondary analyses of a randomized controlled trial 

(RCT) that tested an adherence and symptom management intervention within cancer patients 

that were newly prescribed oral oncolytic agents (OOA).1 Participants in the parent trial were 

recruited from six National Cancer Institute (NCI) designated comprehensive cancer centers. 

After consenting, participants completed baseline telephone interviews, which gathered 

information regarding their OOA regimens; medication beliefs; adherence; medication coverage; 

multimorbid conditions; symptoms; mental and physical functioning; depression; interaction with 

healthcare providers; demographics; and health insurance.  

After completing the baseline interview, participants were randomized to either the 

experimental or control (normal care) group. The experimental group received an adherence 

intervention, which involved an interactive voice response (IVR) system notifying participants 

each time they had to take a dose of their OOAs. Additionally, the experimental group was given 

a symptom management toolkit (SMT). Participants were referred to the SMT when rating the 

severity of a symptom ³4/10 on the Cancer Symptom Experience Inventory (CSEI) severity 

scale.2 The SMT provided strategies for patients to manage the particular symptom. Telephone 

 

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interviews with each participant occurred at four intervals for 12 weeks. After which, medical 

record audits (MRA) were completed by trained auditors at each of the recruitment sites. 

Auditors collected clinical information, including cancer diagnosis history; interaction with 

healthcare providers; cancer treatment regimen prescriptions; toxicities; and non-cancer 

treatment medications.  

The final two manuscripts of this dissertation utilized portions of the data from the parent 

trial study. The purpose of manuscript two was to examine the amount of time between when a 

participant’s initial OOA prescription was written, and when they actually received and started 

taking the OOA medication. The analysis also examined whether certain cancer treatment and 

healthcare system factors may impact the amount of time it took for patients in the parent trial to 

receive their OOAs. Finally, manuscript three examined the relationship between baseline 

antecedent factors (patient, social, disease/treatment, and healthcare system) and temporary 

stoppages of patients’ OOA regimens. Additionally, the analysis explored how BOT-indicator 

variables might moderate this relationship.  

The overall purpose of this dissertation was to add to the limited current body of 

literature regarding BOT, specifically within cancer patients with multimorbid conditions, from a 

conceptual and operational standpoint. Conceptually, this work aimed examine BOT within 

cancer patients with multimorbid conditions, which has not been done within this population. 

Operationally, this dissertation provided new knowledge regarding the challenges that cancer 

patients prescribed OOAs experience in acquiring their medications. Additionally, this work 

examined how multimorbid conditions and increased BOT impacts patients’ continuation of their 

OOA regimens, specifically temporary stoppages of their treatment. This work was completed 

with the hope to add new knowledge and insight into the developing BOT literature, as well as to 

lay a foundation for a program of research focused on decreasing the BOT for cancer patients 

with multimorbid conditions.  

 

 

149 

 

Overview of Manuscript One 

Manuscript one expanded on the burden of treatment conceptual literature that has been done 

by Tran et al.,3-5 Eton et al.,6,7 and Sav et al.8 Unlike these previous works, this manuscript 

focused specifically on the population of cancer patients with multimorbid conditions. The 

conceptual analysis was guided by Rodgers’ concept analysis method, which works well for 

dynamic concepts that continue to change over time.9 The components of BOT can 

continuously change, and therefore, Rodgers’ model was a proper framework to follow. A 

systematic search was carried out across a number of databases and disciplines. In order to 

capture as many eligible articles as possible, certain multimorbid conditions commonly 

associated with cancer were included in as keywords in the search terms, such as diabetes and 

cardiovascular disease. After removing duplicates and articles with no abstracts, all titles and 

abstracts (n = 1938) were screened for eligibility. This abstract screening resulting in 84 full text 

articles, which were then reviewed, in their entirety, for inclusion within the concept analysis. 

After selecting the 29 articles for inclusion, antecedents, attributes and consequences were 

identified from these articles.  

 

The attributes of BOT are divided into two primary components: patient workload and 

patient perspective. Much like the previous BOT works, a major attribute of BOT was patient 

workload. Patient workload is the tasks required of an individual to manage their condition(s). 

Patient workload was further divided into the following subcategories: disease and treatment 

demands, interaction with the healthcare system, and financial responsibilities. These three 

subcategories involve tasks that are required of patients in order to manage their cancer and 

multimorbid conditions. This manuscript found that overall, as the number of multimorbid 

conditions increased, the workload for all three subcategories increased, as well.10-28 The 

attribute of BOT, patient perspective, is concerned with the subjective viewpoint of one’s 

conditions and associated workload. While the majority of the included studies discussed 

workload, only a few studies focused on the patient perspective.13,20,26 These three studies 

 

150 

 

examined how multimorbid conditions and healthcare tasks impact individuals’ daily lives, their 

ability to cope with their disease and workload, and individuals’ perception of their relationship 

with providers.  

 

Before BOT can occur, there are some, or all, antecedent factors that must take place. 

This concept analysis identified four antecedent categories related to BOT, which include 

patient factors, social factors, disease/treatment factors, and healthcare system factors. Certain 

factors can contribute to an increased burden of treatment, such as older age, lack of a support 

system, advanced cancer, multimorbid conditions, and poor access to care.10,12,14,17,18,20,21,23-26,29-

32 While these antecedents can impact attributes of BOT, they can also have a direct impact on 

certain consequences, or outcomes. The consequences most commonly examined in the 

included articles were broken down into four categories: adherence/non-adherence, clinical 

outcomes, treatment modifications, and health and well-being. In general, individuals with 

cancer and multimorbid experienced worse consequences than those with cancer alone.  

 

The purpose of manuscript one was to increase the knowledge within the burden of 

treatment literature, as well as the cancer and multimorbidity literature. In general, the included 

articles concluded that multimorbidity among cancer patients resulted in an increased BOT and 

led to poor consequences, including adherence, clinical outcomes, treatment modifications, and 

health and well-being. The previous BOT works have focused extensively on patient workload, 

but minimally on the patient perspective. Although there were a limited number of included 

articles, this work described how cancer patients with multimorbid conditions perceive the 

impact of their conditions and workload on their daily lives. BOT is a complex concept that will 

continue to be vital as the life expectancy remains high and the number of individuals with 

cancer and multimorbid conditions continues to rise. This work developed a conceptual model, 

in order to provide insight for researchers and clinicians to care for a growing, vulnerable 

population.  

 

 

151 

 

Overview of Manuscript Two 

Manuscript two focused on amount of time patients in the parent trial had to wait to receive their 

OOAs. OOA prescriptions are not as easily filled as other medications. In most cases, only 

certain specialty and mail-order pharmacies are able to fulfill OOA prescriptions.33,34 Therefore, 

patients are not able to easily access medications as they are for common medications, such as 

beta blockers for hypertension. For this reason, this work aimed to examine the time to OOA 

receipt and what barriers might increase this amount of time. This secondary data analysis 

examined the number of days from when patient’s initial OOA prescription was written by their 

oncologist until the patient received the drug in hand. In addition to describing this number of 

days to receipt, this work examined how OOA drug class, type of insurance, recruitment site, 

and whether or not the patient had an out-of-pocket copay for the OOA might impact the 

number of days to receipt.  

 

The results of this secondary analysis showed that patients in this parent trial waited on 

average 10 days (SD = 14.5) to receive their first OOA prescription. Although the mean number 

of days was 10, the range of time for patients to receive their initial prescription was from 0-135 

days. Unadjusted means showed little difference in days to receipt between those with 

private/commercial and government insurance, while individuals without an OOA copay waited 

almost one day longer than those that did have a copay. Patients from one recruitment site 

waited nearly 3.5 days more than the recruitment site with the second longest wait time. 

Patients prescribed OOAs in the “other” OOA drug class waited longer than those prescribed 

any of the other three OOA drug classes. The analysis of variance (ANOVA) showed that 

insurance type made a significant difference on days to receipt, with those that did have a copay 

actually waiting longer, after taking into account the other variables in the model. There was 

also a significant three-way interaction between copay, insurance type, and drug class. After 

running simple interaction effects, the results showed that type of insurance may have a 

difference on the number of days to receipt, depending on whether or not an individual had a 

 

152 

 

copay and what drug class they were prescribed. Thus, the amount of time a patient has to wait 

receive their OOA prescription may vary depending on an individual’s insurance plan, whether 

or not they have an out-of-pocket cost, and the specific OOA they are prescribed.   

 

The majority of patients prescribed OOAs have advanced cancer and have failed 

multiple lines of treatment, and therefore, are in a critical point in their care trajectory. This work 

aimed to determine how many days patients have to wait until receiving their first OOA 

prescription. This work only examined the initial prescription, but not subsequent refills. Patients 

in the parent trial experienced delays in receiving subsequent refill as well, which can cause 

distress and burden for patients during this critical period of care. This work demonstrates the 

need for continued research into the process of patients filling OOA prescriptions, and what can 

be done in practice to decrease the number of days for initial prescription and subsequent refill 

times. Future work should examine each step of the OOA process, in order to determine what 

factors might contribute the greatest number of days to patients’ acquisition time. Additionally, 

future work should continue to assess patient wait times for subsequent refills and what events 

might be delaying patient acquisition, such as prescription error or pharmacy shipment delays.  

Overview of Manuscript Three 

Manuscript three was a secondary analysis of the parent trial that applied the conceptual model 

developed in manuscript one. The purpose of manuscript three was to examine the moderation 

effect of burden of treatment on the relationship between baseline antecedent characteristics 

and temporary stoppages of patients’ OOA regimens. The baseline antecedent factors included 

variables from all four of the antecedent domains. These variables included: patient – age, sex, 

and employment status; social – marital status; disease/treatment – multimorbid conditions and 

OOA drug class; and healthcare system: type of insurance and OOP cost. The burden of 

treatment indicator variables used were an OOA regimen complexity score35 for patient 

workload and patient symptom interference on daily activities2 for the patient perspective. The 

 

153 

 

outcome variable was whether or not an individual experienced a temporary stoppage of their 

OOA regimen during the parent trial.1 

 

The results showed that more than 36% (n = 99) of the patients in the parent trial 

experience a temporary stoppage of their cancer treatment regimen. Forty percent  of patients 

with 4+ multimorbid conditions experienced a temporary stoppage (n = 40), while only 19% of 

individuals with no multimorbid conditions experienced a temporary stoppage (n = 4). The 

moderation models showed that the BOT moderation, interaction terms were non-significant. 

Females (P = .018) and patients prescribed kinase inhibitors (P < .01)  were significantly more 

likely to experience a temporary stoppage during the parent trial.  

 

 In monitoring patients for such a short time, 12 weeks, this secondary analysis showed 

how frequent modifications can occur for patients prescribed OOAs. With the majority of cancer 

patients prescribed OOAs having advanced disease, it is critical for these patients to be 

receiving an effective dose as continuously as possible. However, rest periods are necessary 

for some patients, as this work shows. Although non-significant in the moderation models, this 

work showed a trend of individuals with more multimorbid conditions experiencing a greater 

percentage of temporary stoppages than those with no multimorbid conditions. It was 

hypothesized that BOT would have some impact on the relationship between baseline 

characteristics and treatment modifications. However, this did not hold true, possibly due to the 

utilization of BOT-indicator variables rather than BOT-specific measures. Future research 

should further examine the impact of multimorbid conditions and medications impact on cancer 

patients’ continuation of treatment. Additionally, the development and utilization of BOT-specific 

measures are needed to examine BOT within this population.    

Limitations  

Although this work provides new knowledge and insight to a limited body of literature, it is not 

without its limitations. There are limitations in utilizing a secondary analysis, as well as some 

components of the parent trial. The parent trial recruited patients from the Midwest United 

 

154 

 

States, and therefore, may not be generalizable to the rest of the country. The sample was not 

very racially diverse, with 89% of the sample being Caucasian. The mean age of the sample 

was over 61 years old and only included 8 patients under the age of 35, indicating a need for 

future research to include younger patients. With the majority of recruitment sites being 

academic medical centers, the sample was quite educated. Seventy-three percent of the 

sample completed at least some college work, which is beyond norm for the US. Hematologic 

malignancies were underrepresented in the study, only comprising about 10% of the sample. 

Additionally, the majority of the sample had advanced stage cancer. Because of this sample 

distribution, the parent trial and this secondary analysis may not be generalized to more racial 

diverse, younger, early stage cancers, and hematologic populations.  

Specifically looking at the secondary analysis, there are limitations based on this study 

design. The aims of the parent trial were not designed with the research questions of this 

secondary analysis in mind. Therefore, certain indicator variables were needed, which limits the 

evidence for burden of treatment. Several variables, including temporary stoppages, were 

gathered from the parent trial’s MRAs. The MRAs were limited electronic health data within the 

hospital system of each recruitment center. Therefore, if a patient presented to a clinic or 

hospital outside of the recruitment site hospital system, that data was not attainable. 

Additionally, multimorbid conditions were based on non-cancer prescription medications 

collected from the MRA. Reviewing patients’ ICD-10 multimorbid condition diagnoses may have 

yielded more accurate data. This work provides evidence for research utilizing the concept of 

burden of treatment within cancer patients with multimorbid conditions. However, this future 

work will need study designs and data collection unique to burden of treatment.  

Implications 

Nursing Research 

This work presents one of the first examinations of burden of treatment within cancer patients 

with multimorbid conditions, both from a conceptual and operational standpoint. Conceptually, 

 

155 

 

burden of treatment has been examined within chronic diseases,8 but no such conceptual 

analysis could be found specifically within cancer patients with multimorbid conditions. This 

conceptual work examined the importance of both the patient workload and the subjective 

patient perspective within burden of treatment. Although the antecedents and consequences 

were similar to previous conceptual analyses, this work pointed outs examples of the impact of 

BOT that are unique to cancer patients with multimorbid conditions, such as the impact of BOT 

on the continuation of cancer treatment.  

 

There a few current studies that have examined burden of treatment within cancer 

patients,25,36,37 with one of these articles examining the influence of multimorbid conditions with 

burden of treatment.25 This dissertation is unique in that it is the only work that has specifically 

focused on burden of treatment within cancer patients treated with OOAs, as well as managing 

multimorbid conditions. Manuscript two examined the challenges faced by patients in acquiring 

their OOAs, which has the potential to impact their perspective of their disease and workload, 

while manuscript three examined how BOT and multimorbidity impacts patients’ continuation of 

their cancer treatment, specifically temporary stoppages of OOAs. Research within this 

population is behind the pace needed to make a substantial impact, given the increase in 

prevalence of multimorbid condition and the increase of OOAs entering the chemotherapy 

pipeline each year.38,39 This work has started to address some of the gaps within this scarce 

body of literature.  

 

Two of the primary gaps within the BOT literature are the inclusion and examination of 

patient perspective and the operation of BOT as a whole. As has been briefly discussed, most 

of the work already done focuses on variables that can easily be quantified that contribute to the 

workload of being a patient with a chronic disease. Manuscript one showed that individuals with 

more multimorbid conditions are bothered by their symptoms more than those with cancer 

alone. Manuscript one also found that lack of family support, stress related to being a minority 

(i.e. racial, sexual, or socioeconomic status), lack of spiritual support, and being a burden to 

 

156 

 

caregivers contributed to an increased BOT.13,20,26 The most common concern for patients was 

poor communication with providers. Poor communication will providers was associated with 

increased distress and worse outcomes for individuals with cancer and multimorbid 

conditions.13,20 The contributions from manuscript two on patient perspective show that patient 

workload is not the only contributor to BOT, and that patient perspective can have negative 

impact on one’s ability to manage their conditions and health. However, as has been discussed 

throughout this dissertation, patient workload comprises the majority of the BOT literature. 

Future work should focused on incorporating the patient perspective, in order to fully examine 

the challenges experienced by cancer patients with multimorbid conditions.  

The other gap is how to operationalize burden of treatment, both the patient workload 

and the patient perception of that workload. Patient workload has been operationalized in many 

different ways, including number of medications, interactions with the healthcare system, and 

cost. Manuscript three of this dissertation operationalized workload as the complexity of 

patients’ OOA regimens. While, patient perspective was operationalized by patients’ ratings of 

their symptom interference on daily activities. Due using data from a parent trial, BOT-specific 

measures could not be utilized, nor could other BOT-indicator variables. The BOT-indicator 

variables that were used in manuscript three were non-significant in the regression models. 

Utilizing other BOT-indicator variables or BOT-specific measures may have yielded different 

results. This work points out the need for continued development of BOT-specific measures that 

will may be able to better examine how cancer patients with multimorbid conditions experience 

BOT.  

Looking specifically at operational outcomes of this dissertation, this work focused on patients’ 

time to acquisition of OOAs and temporary stoppages of OOA regimens. Manuscript two 

showed that certain disease/treatment and healthcare system variables may impact the time it 

takes for patients to receive their OOAs. This work only examined the initial OOA prescription, 

but cited examples within the discussion of patients in the trial study having issues receiving 

 

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subsequent OOA refills. Future work in this area should examine each step of the OOA 

acquisition process, as well as difficulties in patients receiving subsequent refills. Patients 

prescribed OOAs are in a vulnerable state of their cancer treatment, and being without their 

drugs can have negative impact on their disease status. Similarly, longer and more frequent 

stoppages can negatively impact patients’ disease status. There was a trend of those with 

multimorbid conditions experiencing a greater proportion of temporary stoppages. Individuals 

prescribed certain drug classes were also more likely to experience temporary stoppages. 

Regarding manuscript two, a future study could include an intervention that includes an 

oncology nurse liaison to follow cancer patients from the time when their initial OOA prescription 

through a number of subsequent refills. The liaison would assist in the initial transition to an 

OOA regimen by communicating with their insurance company to help speed up the coverage 

process, as well as be the advocate for patients when communicating with a specialty or mail-

order pharmacy. The liaison would also work with patients to ensure refills are being processed 

on time and patients are receiving subsequent refills in a timely manner. Different than 

manuscript two, this future study would examine the number of days for each process of the 

OOA acquisition process. The study would also gather data on what type of pharmacy the 

patient is using to fill their OOA prescription, specific OOA drug, insurance information, oncology 

facility they are being treated at, and the financial variables, such as out-of-pocket cost and 

financial assistance needs. This study would provide a more in-depth understanding of the 

challenges cancer patients face in acquiring their OOAs.    

Regarding manuscript three, future work should further examine the workload of cancer 

patients with multimorbid conditions, as well as their perspective of their conditions and 

associated workload. Based on this dissertation, measurement development can begin by 

including the patient workload identified and ask patients to identify the healthcare tasks they 

must complete and how often in order to manage their cancer and other conditions. Additionally, 

the measure would include a subjective component to measure patients perception of their 

 

158 

 

disease and associated workload. This future work would both help to develop a BOT-specific 

measure, as well as to inform future studies that would help to decrease BOT experienced by 

these patients identified by the measure. For example, if patients are having difficulties 

managing their medications between their cancer treatment and other conditions, an 

intervention study utilizing in-home visits from a nurse to provides strategies to effectively 

manage complex medications regimens could be developed. This dissertation found that cancer 

patients with multimorbid conditions are at risk for a greater BOT than those with cancer alone. 

Future research should target the specific needs of this population and help to empower 

patients to effectively manage their cancer and other conditions.  

Nursing Practice 

As the population continues to age and multimorbidity become more prevalent, developing 

education and self-management strategies for cancer and multimorbid conditions will be a vital 

goal for clinicians, especially nurses. With multimorbidity, nurses and other clinicians will be 

tasked with educating individuals about the interactions of their multimorbid conditions and the 

associated medications. When individuals are actually treating cancer and other multimorbid 

conditions, the BOT becomes greater for both the patient and the clinicians. Patients will need 

to have a better and more comprehensive understanding of their current situations and the 

status of their multiple conditions.40-45 Nurses, especially oncology nurses, are in a critical 

position when it comes to educating patients and caregivers about the necessity for effective 

self-management. Better patient education and increased communication between clinicians 

and patients and their caregivers will be vital in working with this population, in order to 

empower patients to effectively manage their cancer and other conditions.  

These three manuscripts have shown evidence for the use of the concept of burden of 

treatment within the growing population of cancer patients with multimorbid conditions. This 

work shows which groups of people may be at risk for a greater burden of treatment and 

subsequently poor outcomes. Clinicians can focus on these patients to provide more education 

 

159 

 

and resources to ensure that they are better equipped to manage their OOAs and multimorbid 

conditions. Cancer patients actively receiving treatment and managing other conditions are in a 

vulnerable state. Currently, there are no standards of care for patients actively being treated 

with OOAs and taking medications for multimorbid conditions. Standards for screening patients 

for appropriateness of OOA treatment regimens should be put into place, while taking into 

account patients’ other conditions and the medications they are prescribed. Nurses are in a 

critical position to help ease the burden of treatment through being an advocate for patients to 

ensure they receive their OOAs in a timely manner or to direct patients towards resources that 

may be able to decrease their BOT, such as social work or support groups.  

Policy 

As individuals with cancer and multimorbid conditions have an increased burden of treatment, it 

may lead to increased adverse events and utilization of healthcare resources. This increased 

burden will lead to greater healthcare spending on the part of the patient and the system itself.46-

51 If the burden of treatment continues to afflict these individuals, acute care facilities must be 

able to management multiple disruptive conditions within an individual, on top of active cancer 

treatment. Having to manage cancer and other multimorbid conditions in an acute care setting 

has been shown to be difficult in stabilizing patient conditions and reducing readmission rates.52-

57 For this reason, policies should be enacted to provide resources for this population, in order 

to reduce the adverse events and resource use brought on by poorly managed conditions.   

 

Looking specifically at individuals prescribed OOAs, this and future work may alter the 

process of patients acquiring their drugs. Manuscript two showed that patients waited on 

average 10 days to receive their drug. While this work did not examine each step of the 

process, previous work found that specialty pharmacy prescription processing and delivery 

accounted for the greatest number of days during the acquisition process.34 Processing time at 

each step may need to be evaluated in order to shorten the acquisition time for patients. 

Additionally, policies may need to be adapting to assist cancer patients with multimorbid 

 

160 

 

conditions in reducing symptoms and modifications to OOA regimens. This may be clinics or 

hospital systems providing additional support for these individuals to help educate patients on 

proper self-management. Therefore, policies may be needed to force insurance companies to 

cover these additional services. Although they may bring about upfront costs, they may bring 

down costs in the long run for both patients and the healthcare system.  

 

Manuscripts one and three focused on the increased burden of treatment experienced 

by individuals with cancer and multimorbid conditions. An increased number of multimorbid 

conditions led to increased burden of treatment, greater non-adherence, poor clinical outcomes, 

more treatment modifications, and worse overall health.11-16,20,23-27 Another finding was that poor 

communication with providers was a primary concern for individuals.13,20 Having cancer and 

multimorbid conditions often means multiple providers, which can be stressful for patients. 

Policies to provide more resources for patients to help decrease poor outcomes and improve 

provider communication, such as nurse navigators, would be beneficial to patients experiencing 

these issues. Policies to require and improve access across electronic health records (EHR) 

would help to improve communication and practice standards across patients’ onolcogy 

providers and other specialists and PCPs. Additionally, policies to increase funding and 

coverage for the use of telehealth to provide patients with more access to providers and quality 

care may help to decrease patient stress and lead to better patient and clinical outcomes. The 

increase in telehealth would allow patients to access effective standards of care, proper 

discharge education and care, and easier access to prescription medications.  

Conclusions  

Burden of treatment is a relatively new concept, but it will continue to become even more vital 

as more individuals are required to manage multiple conditions. This dissertation focused on a 

population that must endure mental and physical struggle every day. This work filled a number 

of gaps within the literature, including: 1) providing a conceptual model for cancer patients with 

multimorbid conditions; 2) emphasizing the importance of patient perspective as a component of 

 

161 

 

BOT; 3) examining BOT within cancer patients with multimorbid conditions; 4) examining the 

impact of disease/treatment and healthcare system variables on the time to acquisition of 

OOAs; 5) examining the relationship between patient, social, disease/treatment, and healthcare 

system factors and temporary stoppages and how BOT might impact this relationship; and 6) 

providing evidence for continued new research and continued development of the concept of 

BOT, specifically within cancer patients with multimorbid conditions. This dissertation found that 

Individuals with cancer and multimorbid conditions are more likely to experience a greater BOT 

than this with cancer alone. Additionally, that certain patients may experience delays in 

receiving their OOAs, based on the type of insurance they have, the specific OOA drug they are 

prescribed, and whether or not they have an out-of-pocket cost. Finally, some patients may be 

more susceptible to experiencing temporary stoppages to their OOA regimens, based on sex 

and the specific OOA drug.  

As the population life expectancy maintains the current rate and more individuals with 

cancer are actively managing other diseases, new research will be needed to empower these 

patients to overcome their burden and to effectively manage their diseases. The current 

literature is far behind the needs of this population. This work provided new insight to help 

address some of the gaps with the BOT literature. These gaps include examining BOT within 

cancer patients with multimorbid conditions; the challenges cancer patients experience in 

acquiring their OOAs; and how including examining BOT within cancer patient with multimorbid 

conditions and examining patient perspective, and examining how BOT might impact the 

relationship between patient’s baseline characteristics and the modification of their OOA 

treatment regimen. The goals of this dissertation were to address these gaps and to build a 

foundation for a program of research that is focused on decreasing the burden of treatment 

within this population and allowing patients to take control of their cancer and multimorbid 

conditions.  

 

 

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