Asiedu-Ofei Akua Afriyie.Pdf

CANCER PAIN ASSESSMENT, MANAGEMENT AND EFFECT ON THE QUALITY

OF LIFE OF PATIENTS AT A TERTIARY HOSPITAL IN GHANA

A THESIS SUBMITTED IN FULFILLMENT OF THE REQUIREMENTS FOR THE

DEGREE OF

DOCTOR OF PHILOSOPHY IN PHARMACOLOGY

in the

Department of Pharmacology

Faculty of Pharmacy and Pharmaceutical Sciences

College of Health Sciences

AKUA AFRIYIE ASIEDU-OFEI

KWAME NKRUMAH UNIVERSITY OF SCIENCE & TECHNOLOGY,

KUMASI

JUNE , 2019

DECLARATION I declare that I am the sole author of this thesis, and that this work has not been submitted for any other degree anywhere. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I accept that my thesis may be made electronically available to the public.

………………………….. ………………………….. Akua Afriyie Asiedu-Ofei Date (PG2355414)

Certified by:

………………………….. ………………………….. Prof. Eric Woode Date (Principal Supervisor)

Certified by:

………………………….. ………………………….. Prof. Dr. David Darko Obiri Date (Head of Department)

ii ABSTRACT Background: Despite extensive technological advancements in medicine in recent years, cancer still remains poorly managed resulting in pain and affecting adversely the quality of life (QoL) of patients. Additionally, evidence suggests that effective cancer pain management is largely inadequate in low and middle income countries such as Ghana. Aim: This study seeks to assess the severity and management of cancer pain at the Komfo Anokye Teaching Hospital (KATH), Kumasi, Ghana. Methods: Using a structured questionnaire, the socio-demographic data, clinical characteristics, and drug history of 204 consenting ambulatory oncology patients, aged 18 years or older, attending clinic at the Oncology Directorate, KATH were obtained from medical folders and the Hospital Administration Management System (HAMS) from January – December, 2015. Structured interviews were also conducted for the patients using the Brief Pain Inventory Long Form (BPI-LF) and the World Health Organization Quality of Life-Brief version (WHOQoL- Bref) to evaluate patients‘ level of self-reported pain and how pain affects their quality of life. Data obtained was analysed using reliability and validity tests, parametric and non-parametric tests. Results: Majority of patients were females (82.8%), married (56.6%), Christians (89.3%), and employed (86.8%), with mean age and parity of 53.54 years (SD = 15.457) and 4.39 children (SD = 3.118) respectively. Most patients (81.4%) subscribed to the National Health Insurance Authority (NHIA) registration to cover medical expenses. Diagnosis for breast cancer (37.7%), stage III/ IV disease (63.3%) and metastases (32.5%) were predominantly identified. Only 9.1% of patients had positive family history of cancer while 8 in 10 (79.6%) patients had Eastern Cooperative Oncology Group performance status of 1. Hypertension was the predominant comorbid condition identified in 19.6% of the patients. Dual chemotherapeutic agents combination therapy (46.5%) and mono analgesic therapy (51.1%) were most used. Internal consistency reliability of the BPI-LF and WHOQoL-Bref were 0.876 and 0.910 respectively. More than half of the patients (63.7%) reported moderate pain while 28.4% reported severe pain. Cancer pain interfered highly with sleep (46.2%) and general activity (42.5%) of patients. Common sites of cancer pain reported by patients were abdomen (20.3%) and left breast (12.4%). A set of patients‘ factors did not predict their overall quality of life and the regression model did not adequately fit the data (R2= 0.44). Conclusion: Patients predominantly reported moderate cancer pain. Pain management was mostly inadequate in the patients and pain significantly affected the quality of life of the patients.

iii ACKNOWLEDGEMENTS My greatest thanks and appreciation go to God Almighty for the grace to go through this study. I wish to show special appreciation to my principal supervisor, Professor Eric Woode for his encouragement, containment, and supervision. My profound gratitude also goes to Dr. Ernest Bawuah Osei-Bonsu and Pharm. Dr. Kofi Boamah Mensah for their useful comments, insights and guidance. I am also grateful to Dr. Robert Peter Biney for his insightful reviews of this work. My humble thanks go to all patients who took part in the study. For many of them, it is a life threatening situation and a personal crisis but notwithstanding these, they sacrificed their time to share their stories. I am honoured and extremely humbled to have had the opportunity of listening to the experiences of those who endure pain in their daily lives and the struggles that this brings in their lives. I am very thankful to my Head of Department, Prof. Dr. David Obiri Darko and all lecturers in the Department of Pharmacology, Faculty of Pharmacy and Pharmaceutical Sciences, KNUST especially Prof. George Asumeng Koffuor for their words of encouragement and support. To all my ―pharmacology comrades‖ especially Mrs. Theresa Frimpong and Dr. Mrs. Linda Gyanfosu Asamoah, I say a very big thank you for all the love and encouragement. To all staff at the Oncology Directorate, Komfo Anokye Teaching Hospital, I say a big thank you. I am particularly indebted to my family especially my dear husband; Mr. Gerhard Asiedu-Ofei, my mum; Ms. Mercy Ofori, my sister; Akua Kusiwaa Karikari, my aunties; Sarah Ofori Armaah and Janet Antwi and my father-in-law; Mr Albert Asiedu- Ofei for their unflinching love and support over the years. I am also thankful to all staff at Kumasi Technical University especially Dr. Francis Mainoo-Sarpong, Prof. Ing. Osei Wusu-Achiaw, Mr. Kwadwo Ntiamoah-Sarpong, Dr. Mrs. Patricia Owusu-Darko, Dr. Amina Abubakar and Mr. Edmund Ayensu. I am also appreciative of the help offered to me by Dr. Cedric Amengor, Mr. Bede Yaw Ansiri- Asafoakwaa, Dr. Nicolas Oppong-Mensah and Dr. Alfred Owusu. To Harold, Jeremy and my beautiful daughter; Zara Giselle, you guys are my pride, inspiration and source of strength to achieve greatness. Thanks for being there for me and being so loving. To all loved ones and well- wishers, I am grateful and thanks for all your help. I love you all!!

iv TABLE OF CONTENTS

DECLARATION ...... ii ABSTRACT ...... iii ACKNOWLEDGEMENTS ...... iv TABLE OF CONTENTS ...... v LIST OF TABLES ...... ix LIST OF FIGURES ...... xi ABBREVIATIONS/ ACCRONYMS ...... xiii CHAPTER ONE ...... 1 1.0 GENERAL OVERVIEW ...... 1 1.1 Background of the study ...... 1 1.2 Problem statement ...... 1 1.3 Justification of the study ...... 2 1.4 Research hypothesis of the study ...... 2 1.5 Aim and specific objectives of the study ...... 2 CHAPTER TWO ...... 3 2.0 LITERATURE REVIEW ...... 3 2.1 Epidemiology of cancer and cancer pain ...... 3 2.2 Concept of cancer ...... 3 2.2.1 Carcinogenesis ...... 4 2.2.2 Types of cancer ...... 7 2.2.3 Signs and symptoms of cancer ...... 7 2.2.4 Classification of cancer ...... 7 2.2.4.1 Carcinomas ...... 7 2.2.4.2 Sarcomas ...... 7 2.2.4.3 Myelomas ...... 7 2.2.4.4 Lymphomas ...... 8 2.2.4.5 Leukemias ...... 8 2.2.4.6 Mixed types of cancer ...... 8 2.2.5 Staging of cancer ...... 8 2.3 Pharmacological treatments of cancer ...... 8 2.3.1 Cytotoxic or cancer chemotherapeutic drugs ...... 9 2.3.1.1 Alkylating agents and related compounds ...... 9 2.3.1.2 Antimetabolites ...... 9 2.3.1.3 Antitumor (cytotoxic) antibiotics ...... 10 2.3.1.4 Plant alkaloids ...... 10 2.3.1.5 Hormones and hormone antagonists ...... 10 2.3.1.6 Radioactive isotopes ...... 11 2.3.1.7 Monoclonal antibodies ...... 11 2.3.1.8 Topoisomerase inhibitors ...... 12 2.3.1.9 Miscellaneous agents ...... 12 2.3.2 Combination chemotherapy ...... 12 2.3.3 Toxic effects of chemotherapeutic agents ...... 12 2.3.4 Combined cancer treatment modalities ...... 13 2.3.5 Factors influencing cancer treatment options ...... 13

v 2.4 Cancer pain ...... 14 2.4.1 Causes of cancer pain ...... 15 2.4.2 Types of cancer pain ...... 15 2.4.2.1 From histological standpoint ...... 15 2.4.2.2 By onset and duration ...... 16 2.5 Assessment of cancer pain ...... 16 2.5.1 Cancer pain assessment tools ...... 17 2.5.1.1 Patient self-report pain assessment tools ...... 17 2.5.1.1.1 One-dimensional pain assessment tools ...... 18 2.5.1.1.2 Multidimensional cancer pain assessment tools ...... 20 2.6 Management of cancer pain ...... 21 2.6.1 Pharmacological cancer pain management ...... 21 2.6.1.1 WHO analgesic ladder ...... 21 2.6.1.2 Analgesics ...... 22 2.6.1.2.1 Non-opioid analgesics ...... 22 2.6.1.2.2 Adjuvant analgesics ...... 23 2.6.1.2.3 Opioid analgesics (narcotics) ...... 24 2.6.2 Non-pharmacological cancer pain management strategies ...... 25 2.7 Pain Management Index (PMI) of patients ...... 25 2.8 Challenges to effective cancer pain management ...... 26 2.9 Effects of cancer pain on the quality of life of patients ...... 26 2.9.1 Quality of life assessment tools ...... 26 2.9.1.1 World Health Organization Quality of Life (WHOQoL) instruments ... 26 2.9.1.1.1 The World Health Organization Quality of Life- Brief version ...... 27 2.9.1.1.1.1 Domains/ facets of the World Health Organization Quality of Life- Brief version ...... 27 2.10 Conceptual framework of the study ...... 27 CHAPTER THREE ...... 29 3.0 SOCIODEMOGRAPHICS, CLINICAL CHARACTERISTICS AND DRUG HISTORY OF PATIENTS...... 29 3.1 Introduction ...... 29 3.2 Methodology ...... 30 3.2.1 Ethical clearance for the study ...... 30 3.2.2 Study setting ...... 30 3.2.3 Study design ...... 30 3.2.4 Sources of data ...... 31 3.2.5 Sampling procedure and sample size determination ...... 31 3.2.6 Data collection instrument ...... 32 3.2.7 Pretesting of data collection instrument ...... 32 3.2.8 Data collection procedure ...... 32 3.2.8.1 Social history of patients ...... 32 3.2.8.2 Past medical history of patients ...... 33 3.2.8.3 Patients‘ family history of cancer ...... 33 3.2.8.4 History of presenting compliant of patients ...... 33 3.2.8.5 Drug history of patients ...... 34 3.3 Data analyses ...... 34 3.4 Results ...... 35 3.4.1 Socio-demographic characteristics of patients ...... 35 Figure 3.1: Age distribution per gender of patients involved in the study ...... 36

vi 3.4.2 Patients‘ family history of cancer ...... 37 3.4.3 History of presenting compliant of patients ...... 37 3.4.4 Past medical history of patients ...... 38 3.4.5 Drug history of patients ...... 38 3.4.5.1 Relationships between medications and primary cancer sites of patients ...... 40 3.5 Discussion ...... 42 3.6 Conclusion ...... 46 3.7 Recommendations ...... 46 CHAPTER FOUR ...... 47 4.0 CANCER PAIN ASSESSMENT IN PATIENTS ...... 47 4.1 Introduction ...... 47 4.2 Methodology ...... 47 4.2.1 Study population ...... 47 4.2.2 Sampling procedure and sample size determination ...... 48 4.2.3 Data collection instrument and translation ...... 48 4.2.4 Pre-testing of the data collection instrument ...... 49 4.2.5 Patients‘ consent/ ethical considerations ...... 49 4.2.6 Procedure for interviews ...... 49 4.2.7 Inclusion criteria of the study ...... 50 4.2.8 Exclusion criteria of the study ...... 50 4.2.9 Pre-data analysis procedures ...... 50 4.3 Data analyses ...... 51 4.4 Results ...... 52 4.4.1 Sociodemographic characteristics of patients ...... 52 4.4.2 History of presenting compliant of patients ...... 53 4.4.3 Past surgical history of patients ...... 55 4.4.4 Drug history of patients ...... 56 Parametric tests ...... 66 Relationship between variable ...... 71 4.5 Discussion ...... 73 4.6 Conclusion ...... 78 4.7 Recommendations ...... 78 CHAPTER FIVE ...... 79 5.0 HEALTH-RELATED QUALITY OF LIFE of patients ...... 79 5.1 Introduction ...... 79 5.2 Methodology ...... 79 5.2.1 Data collection instrument ...... 80 5.2.2 Pre-data analysis procedures ...... 80 5.3 Results ...... 80 5.3.1 Descriptive analyses ...... 80 Socio-demographic characteristics of patients ...... 80 Health and quality of life ratings of patients ...... 80 Domains ...... 81 Measures of central tendency and spread of individual items/ domains ...... 87 Additional information on the questionnaire ...... 88 5.3.2 Internal consistency (reliability) ...... 88 5.3.3 Non-parametric tests ...... 88

vii 5.3.4 Parametric tests ...... 89 5.4 Discussion ...... 93 5.5 Conclusion ...... 97 5.6 Recommendations ...... 97 CHAPTER SIX ...... 98 6.0 GENERAL DISCUSSION...... 98 6.1 Limitations of the study ...... 102 CHAPTER SEVEN ...... 104 7.0 CONCLUSIONS AND RECOMMENDATIONS ...... 104 7.1 Conclusions ...... 104 7.2 Recommendations for future research: ...... 104 REFERENCES ...... 105 APPENDIX ...... 121

viii LIST OF TABLES Table 2.1: Causes of cancer ...... 6 Table 2.2: Description of the grades of ECOG performance status of cancer patients* ...... 14 Table 2.3: Classes of adjuvant analgesics used for cancer pain management* ...... 24 Table 3.1: Frequency statistics for current occupations of patients involved in the study ... 36 Table 3.2: Frequency statistics for performance status of patients (n = 204) ...... 36 Table 3.3: Frequency statistics for patients‘ use of chemotherapeutic agents ...... 39 Table 3.4: Frequency statistics for patients‘ use of chemotherapeutic agent classes ...... 39 Table 3.5: Frequency statistics for prescribed analgesics for patients‘ (n=204) ...... 40 Table 3.6: Relationship between chemotherapeutic agents and primary cancer sites of patients ...... 40 Table 3.7: Relationship between classes of chemotherapeutic agents and primary cancer sites of patients ...... 41 Table 3.8: Relationship between commonly prescribed analgesics and primary cancer sites of patients ...... 41 Table 4.1: Sociodemographic characteristics of patients ...... 52 Table 4.2: Frequency statistics for pain location of patients (n=153) ...... 54 Table 4.3: Reported severity of pain intensity items of patients (n=102) ...... 54 Table 4.4: Pain intensity and functional interference indices items ...... 54 Table 4.5: Reported pain quality of patients ...... 55 Table 4.6: Reported functional interference of patients (n=102) ...... 55 Table 4.7: Frequency statistics for types of surgery undergone by patients (n = 34) ...... 56 Table 4.8: Pain Management Index of patients ...... 58 Table 4.9: Reliabilities of the pain intensity index, functional interference index and whole scale...... 58 Table 4.10: ―Corrected Item-Total Correlation‖ coefficients and ―Cronbach's alpha if item deleted‖ values for pain intensity and functional interference indices items ...... 59 Table 4.11: Spearman‘s rank order correlation between functional interference items ...... 60 Table 4.12: Mann-Whitney U analysis of the surgical history of patients with pain intensity index ...... 61 Table 4.13: Mann-Whitney U analysis of the surgical history of patients with functional interference index ...... 61 Table 4.14: Mann-Whitney U analysis of the brachytherapy history of patients with pain intensity index...... 62 Table 4.15: Mann-Whitney U analysis of the brachytherapy history of patients with functional interference index ...... 62 Table 4.16: Kruskal-Wallis H analysis of age groups with pain intensity index ...... 64 Table 4.17: Kruskal-Wallis H analysis of age groups with functional interference index ... 64 Table 4.18: Kruskal-Wallis H analysis of marital status with pain intensity index ...... 64 Table 4.19: Kruskal-Wallis H analysis of marital status with functional interference index ...... 64

ix Table 4.20: Kruskal-Wallis H analysis of current occupation of patients with pain intensity index ...... 65 Table 4.21: Kruskal-Wallis H analysis of current occupation of patients with functional interference index ...... 65 Table 4.22: Kruskal-Wallis H analysis of primary site of cancer of patients with pain intensity index ...... 65 Table 4.23: Kruskal-Wallis H analysis of primary site of cancer of patients with functional interference index ...... 66 Table 4.24: Independent Samples t-test analysis of pain intensity items and gender of patients ...... 68 Table 4.25: Types of analgesics and pain intensity and functional interference indices of patients ...... 69 Table 4.26: Types of analgesics and pain intensity items ...... 70 Table 4.27: Results of ANOVA for functional interference index (dependent variable) and constant and pain severity index (predictors) ...... 70 Table 4.28: Regression coefficientsa ...... 71 Table 4.29: Relationship between prescribed analgesics and gender of patients ...... 71 Table 4.30: Relationship between gender and Pain Management Index of patients ...... 72 Table 4.31: Relationship between commonly prescribed analgesics and reported pain intensity of patients ...... 72 Table 4.32: Relationship between reported common pain location and pain intensity of patients ...... 72 Table 5.1: Domains of the WHOQoL-Bref ...... 87 Table 5.2: Spearman‘s rank order correlation analysis between domains of the WHOQoL-Bref ...... 88 Table 5.3: Independent Samples t-test between domains of the WHOQoL-Bref and gender of patients ...... 90 Table 5.4: One-way ANOVA analysis of quality of life domains and groups of prescribed analgesics ...... 91 Table 5.5: One-way ANOVA analysis for overall quality of life (dependent variable) and independent variables ...... 92 Table 5.6: Regression coefficientsa ...... 92

x LIST OF FIGURES Figure 2.1: The 4 stages of carcinogenesis. Adopted from Weston A and Harris CC. Multistage Carcinogenesis. In: Kufe DW, Pollock RE, Weichselbaum RR, et al., editors. Holland-Frei Cancer Medicine. 6th Edition. Hamilton (ON): BC Decker; 2003...... 5 Figure 2.2: Visual Analogue Scale (VAS) for cancer pain assessment in adults...... 18 Figure 2.3: Verbal Descriptor Scale (VDS) for cancer pain assessment in adults...... 19 Figure 2.4: Numerical Rating Scales (NRS) for cancer pain assessment in adults...... 19 Figure 2.5: Faces Pain Scale- Revised (FPS-R) for cancer pain assessment in children. .... 20 Figure 2.6: Wong-Baker FACES Pain Rating Scale for cancer pain assessment in children...... 20 Figure 2.7 Conceptual framework of the study ...... 28 Figure 3.1: Age distribution per gender of patients involved in the study...... 36 Figure 3.2: Primary cancer sites of patients ...... 37 Figure 3.3: Classes of cancer (A), Stages of cancer (B) ...... 37 Figure 3.4: Comorbid conditions of patients ...... 38 Figure 4.1: Patients‘ pain duration after taking pain medication...... 57 Figure 4.2: Patients‘ pain medication intake within a 24 hour period...... 57 Figure 4.3: Complementary pain alleviation techniques used by patients ...... 57 Figure 4.4: Spearman‘s rank order correlation between the four pain intensity items. Correlation is significant at the 0.01 level (2-tailed) ...... 60 Figure 4.5: Pearson correlation analysis between pain severity index and functional inteference index. Correlation is significant at p < 0.01 (2-tailed) ...... 66 Figure 4.6: Scatter matrix between Pain Management Index, functional interference and pain intensity indices after controlling for a covariate. Correlation is significant at p < 0.01 ...... 67 Figure 4.7: Scores for patients with breast and gynaecological cancers; a. functional interference items and b. pain intensity items. Bars represent M ± SD. * indicate p < 0.05 ...... 68 Figure 5.1: A Quality of life ratings by patients and B. health ratings by patients...... 81 Figure 5.2: Summary of physical health domain: (A) degree of restriction of patients‘ activities by pain (B) patients‘ need for medical treatment to function in their dialy lives (C) patients‘ energy levels to function in their dialy lives and (D) extent of patients‘ movement around ...... 82 xi Figure 5.3: Summary of physical health domain: (A) extent of patients‘ sleep disturbance (B) patients‘ capacity for work and (C) patients‘ ability to perform activities ...... 83 Figure 5.4: Summary of psychological health domain: (A) extent of patients‘ enjoyment of life (B) extent of patients‘ meaningfulness of life (C) extent of patients‘ concentration and (D) extent of patients‘ acceptance of body appearance ...... 84 Figure 5.5: Summary of psychological health domain: (A) patients‘ personal satisfaction and (B) patients‘ negative feeling...... 84 Figure 5.6: Summary of social relationship domain: (A) patients‘ personal relationships (B) patients‘ rating of sex life and (C) patients‘ rating of social support ...... 85 Figure 5.7: Summary of environmental health domain: (A) patients‘ feeling of safety in daily lives, (B) patients‘ feeling of healthy physical environment, (C) patients‘ financial capacity and (D) patients‘ access to information ...... 86 Figure 5.8: Summary of environmental health domain: (A) patient‘s opportunity for leisure (B) patients‘ satisfaction with home (C) patients‘ access to health services and (D) patients‘ mode of transportation ...... 87 Figure 5.9: Spearman‘s rank order correlation analysis showing significance between domains of the WHOQoL-Bref. ** Correlation is significant at the 0.01 level (2-tailed), *** Correlation is significant at the 0.001 level (2-tailed), ns = non-significant ...... 89 Figure 5.10: Independent Samples t-test for domains of the WHOQoL-Bref for breast and gynecological cancer patients. Bars represent means and standard deviations ...... 90

xii ABBREVIATIONS/ ACCRONYMS 131I Radioactive Iodine

5FU 5-Fluorouracil

6-MP 6-Mercaptopurine

6-TG 6 -Thioguanine

AC Doxorubicin

ADLs Activities of Daily Living

AFP Alpha-fetoprotein

AIDS Acquired Immune Deficiency Syndrome

ALL Acute Lymphoblastic Leukemia AML Acute Myelogenous Leukemia ANOVA Analysis of Variance

APS American Pain Society

ARA-C Cytarabine

ARVs Antiretrovirals

As Arsenic ASA Aspirin

ASCO American Society of Clinical Oncology

ATC ―Around the clock‖

BCG Bacilllus Calmette and Guerin

BCNU Carmustine

BCTOS Breast Cancer Treatment Outcomes Scale

BMI Body mass index

BP Blood pressure

BPFS Back Pain Function Scale

BPI Brief Pain Inventory

xiii BPI-LF Brief Pain Inventory- Long-Form

BPI-SF Brief Pain Inventory- Short-Form

BRCA1 Breast cancer susceptibility gene 1

BRCA2 Breast cancer susceptibility gene 2 BTcP Breakthrough cancer pain

C. sinensis Clonorchis sinensis

CAM Complementary and Alternative Medicine

CB Carboplatin

CBT Cognitive behavioural therapy

CCA Cholangiocarcinoma

CCNU Lomustine Cd Cadmium

CD4 Cluster of differentiation 4 CDC Centre for Disease Control

CHRPE Committee on Human Research, Publications and Ethics

CI Confidence Interval

Cis/Tis Carcinoma in situ

CITC Corrected Item-Total Correlation CML Chronic Myelogenous Leukemia

CNS Central Nervous System

COX Cyclooxygenase

COX-1 and COX-2 Isoforms of cyclooxygenase enzymes CP Cisplatin

CPM Cyclophosphamide

CPPS Cancer Pain Prognostic Scale

CPT-11 Irinotecan

Cr Chromium

xiv CRP Cognitive Risk Profile

CT/ CAT Computerized tomography

CTX/ CTx Chemotherapy CVS Cardiovascular system

DA or DAC Daunorubicin

DALYs Disability - Adjusted Life Years

DES Diethylstilbestrol

DF118 Dihydrocodeine dFdCyd Gemcitabine

DNA Deoxyribonucleic acid

DOM Domain

DOX Docetaxel DTIC/ DIC Dacarbazine

E3 Estriol

EBV Epstein- Barr Virus

ECOG Eastern Coorporative Oncology Group

ECS-CP Edmonton Classification System for Cancer Pain

EFAT-2 Edmonton Functional Assessment Tool-2

EGFR Epidermal Growth Factor Receptor

EPR Epirubicin

EQ-5D EuroQoL Quality of Life Scale ER/ XL/ XR/ XT Extended-release

ETS Environmental tobacco smoke

FDA Food and Drugs Authority

FPS-R Faces Pain Scale- Revised

GI/ GIT Gastrointestinal tract

xv GOP Gemcitabine, Oxaliplatin and Paclitaxel

GRH Gonadotrophin-releasing hormone

H. pylori Helicobacter pylori

HADS Hospital Anxiety and Depressive Scale

HAMS Hospital Administration Management Systems

HBV Hepatitis B Virus

HCV Hepatitis C Virus

HD Hodgkin‘s lymphoma/ disease HER-2/ neu Human epidermal growth factor receptor

HIV Human Immunodeficiency Virus

HNRS Horizontal Numerical Rating Scale

HPV Human papillomaviruses

HRQoL Health-Related Quality of Life

HRT Hormone-Replacement Therapy

HSD Honestly significant difference

HSV2 Herpes Simplex Virus 2

HTLV Human T- Lymphotropic Virus

HUI-3 Health Utilities Index

HVAS Horizontal Visual Analogue Scale IA/ IAC Idarubicin

IADLs Instrumental Activities of Daily Living

IASP International Association for the Study of Pain IBM Corp International Business Machines Corporation

ICEC International Cancer Expert Corps

IFO Ifosfamide

IM Intramuscular

xvi IMMPACT Initiative on Methods, Measurement, and Pain Assessment in

Clinical Trials

IR Immediate-release/ infrared radiation

ISBS Injection-site burning and stinging

ISCO-08 International Standard Classification of Occupations 2008 ISHD/ IHD Ischemic Heart Disease

IV Intravenous

K Kappa receptor

KATH Komfo Anokye Teaching Hospital

KBTH Korle-Bu Teaching Hospital

KNUST Kwame Nkrumah University of Science and Technology

KPS Karnofsky Performance Status KS Kaposi‘s sarcoma

LHRH Luteinizing hormone-releasing hormone

LMIC Low-and Middle-Income Countries

LMP Last menstrual period m2 Square metre mAbs/ MAB Monoclonal antibodies

MALT Mucosa-Associated Lymphoid Tissue

MAT- PC Medication Assessment Tool for Cancer Pain Management

MOS Medical Outcome Study 116 item core set

MOS- SF- 36 Medical Outcomes Study Short-Form 36-Item

MPQ McGill Pain Questionnaire

MRI Magnetic resonance imaging

Mtx Methotrexate MWW Mann-Whitney-Wilcoxon

xvii NCI National Cancer Institute

NGOs Non-governmental organisations

NHIA National Health Insurance Authority

NHL Non-Hodgkin‘s lymphoma

NHP Nottingham Health Profile

Ni Nickel

NMDA N-Methyl-D-Aspartate

NPC Nasopharyngeal carcinoma

NRS Numerical Rating Scale NSAIDs Non-Steroidal Anti-Inflammatory Drugs

O. viverrini Opisthorchis viverrini

OSW-2 Oswestry Disability Index 2 OTC Over-the-counter

PAF Pain assessment form

PAPP-A Pregnancy-associated plasma protein-A

PAX Paclitaxel

PCA Patient-Controlled Analgesia

PCBs Polychlorinated biphenyls

PCEA Patient-Controlled Epidural Analgesia

PCINA Patient-Controlled Intranasal Analgesia

PG Prostaglandins

PMI Pain Management Index

PMVs Personal Mobility Vehicles

PNS Peripheral Nervous System

PO Per oral

xviii POABS-CA Pain Opioid Analgesics Beliefs Scale- Cancer

PPE Palmar-plantar erythrodysesthesia

PQAS Pain Quality Assessment Scale

PR Per rectum pRb Retinoblastoma protein Prn pro re nata PROMIS Patient Reported Outcome Measurement System

PROMs Patient Reported Outcome Measures

PROs Patient Reported Outcomes

PS Performance status

PTSD Post-traumatic stress disorder

PTSS Post-traumatic stress symptom

QALYs Quality- Adjusted Life Years

QOL/ QoL Quality of life

QOLS Quality of Life Scale

RNA Ribonucleic acid

RPS Regional Pain Scale

S. haematobium Schistosoma haematobium SC Subcutaneous SCD Sickle- cell disease SD Standard Deviation SF-MPQ Short- Form - McGill Pain Questionnaire SOB Shortness of breath SPSS Statistical Package for Social Scientists SR Sustained-release STAR Screening Tool for Addiction Risk STZ Streptozotocin TCA Tricyclic antidepressant

xix TIAs Transient Ischemic Attacks TMX Tamoxifen TMZ Temozolomide TNM Tumour Node Metastasis TSPA Thiotepa TTH Tamale Teaching Hospital USA/US United States of America UV Ultraviolet VAS Visual Analogue Scale VDS Verbal Descriptor Scale VM-26 Teniposide VNRS Vertical Numerical Rating Scale VP-16 Etoposide VRS Verbal Rating Scale VVAS Vertical Visual Analogue Scale WHO World Health Organization WHOQoL-Bref World Health Organization Quality of Life- Brief version W-QLI Wisconsin Quality of Life Index ZDX Goserelin

CHAPTER ONE

1.0 GENERAL OVERVIEW This chapter reviews the background of the assessment and management of cancer pain, problem statement, justification, research hypothesis, aim, specific objectives and limitations of the study.

1.1 Background of the study Pain is a common, complex, debilitating and subjective experience which occurs in more than 70% of patients with advanced cancer (Prommer, 2015). Cancer pain is usually gravely underestimated and undermanaged in patients (Prommer, 2015). Although cancer may be a terminal disease associated with pain and suffering, a lot can be done medically in terms of pain relief to make the last moments of sufferers better and relatively pain-free. Pain, being a subjective self-reported experience requires that the patient should be the key assessor of his/her pain. Regular assessments and reassessments of cancer pain with assessment tools like the Brief Pain Inventory (BPI) allows treatment regimens to be monitored and modified promptly to achieve optimum cancer pain control (Kumar, 2011). For cancer patients, adequate psychosocial support is also vital as psychosocial factors like fear, anxiety, depression, and anger can exacerbate their experience of pain and affect their quality of life (QoL/ QOL) which when assessed can lead to good clinical interventions.

1.2 Problem statement Effective pain management in cancer patients has requirements which are not fully met in the country (Wiredu and Armah, 2006). These include:

• Inadequate number of standard treatment centres for the increasing number of reported cancer cases

• Inadequate number of trained personnel

• Limited access to modern diagnostic and treatment equipment

1.3 Justification of the study Relatively not much information is documented about the extent of cancer pain assessments and management in low and middle income countries like Ghana. The results of this study is relevant to promulgate effective health policies that could drive existing ones in Ghana.

1.4 Research hypothesis of the study Based on the background of the study and problems identified, it can be hypothesied that cancer pain is inadequately managed in Ghana and that standard cancer pain assessment tools are probably not used. It could also be speculated that cancer patients generally have poor quality of life. This study would therefore clear some of these speculations and answer some questions such as those; stated below, that baffle the minds of some patients and other individuals: 1. Does sociodemographic and clinical characteristics affect incidence of cancer in patients in this study?

2. How adequate is cancer pain assessment in patients involved in this study?

3. How adequate is cancer pain relief in the study patients?

4. How does cancer pain affect the quality of life of patients involved in this study and how can it be improved?

1.5 Aim and specific objectives of the study This study seeks to access the severity/ intensity and management of cancer pain at the Komfo Anokye Teaching Hospital in Ghana. To this end, the following specific objectives were considered: i. To determine whether socio-demographic and clinical characteristics of cancer outpatients directly affected their incidence of cancer. ii. To document management approaches of cancer pain at the Komfo Anokye Teaching Hospital. iii. To assess pain intensity levels of cancer outpatients at the Komfo Anokye Teaching Hospital. iv. To assess the effects of cancer pain on the quality of life of patients at the Komfo Anokye Teaching Hospital. 2

CHAPTER TWO

2.0 LITERATURE REVIEW This chapter reviews pertinent literature on the epidemiology of cancer and cancer pain, pharmacological treatment of cancer, assessment and management of cancer pain, the effect of pain on the quality of life of cancer patients and the conceptual framework of the study.

2.1 Epidemiology of cancer and cancer pain Even by conservative estimates, well over 10 million people worldwide are diagnosed with cancer annually (Black et al., 2011). It has been predicted that by the year 2020, over 20 million new cases of cancer will be diagnosed every year (Kvale et al., 2007; Peuckmann et al., 2007). In the United States of America (USA), cancer is the second leading cause of death contributing to 25% of deaths yearly (Jemal et al., 2008). It is known that out of the 58 million people who die of cancer every year, 45 million are from developing countries (Daher, 2011). Of serious concern however is the fact that many Ghanaians die with undiagnosed cancer every year (Abotchie and Shokar, 2009).

Pain afflicts millions of cancer patients globally every year in spite of major advances in cancer pain management in recent years (Greco et al., 2014). Figures relating to the incidence of pain in cancer patients are mostly underestimated as many patients do not report their pain and/ do not assess treatment (Greco et al., 2014). Pain occurs in 20- 50% of patients at the time of cancer diagnosis (Prommer, 2015) and in approximately 80% at the level of advanced disease (Prommer, 2015; Ogboli-Nwasor et al., 2013).

2.2 Concept of cancer

Cancer (also known as malignant neoplasm, malignant tumour or malignancy) is a group of diseases typified by uncontrolled or abnormal multiplication and spread of the body‘s own cells in certain parts of the body. Cancer cells do not respond to apoptosis (programmed cell death/ cell suicide) (Evan and Vousden, 2001; Lopez and Tait, 2015) or senescence (aging) (Campisi, 2001; Campisi, 2005). Apoptosis and senescence are normal processes that regulate cell growth, proliferation and survival. Cancer cells therefore cannot carry out the normal physiological functions of normal or

differentiated cells and are thus are often described as poorly differentiated (immature, immortal).

Although both benign and malignant tumours exhibit uncontrolled proliferation, malignant tumours can be differentiated from benign tumours by their properties of dedifferentiation, invasiveness, metastases and angiogenesis (Gupta and Massagué, 2006; Ryan et al., 2013; Hadi et al., 2008; Delongchamps et al., 2008). The appearance of these abnormal characteristics in cancer cells reflect altered patterns of gene expression resulting from genetic mutations. There are two main categories of genetic changes that lead to cancer formation: stimulation of proto-oncogenes to oncogenes and inactivation of tumour suppressor genes (anti-oncogenes). These genetic changes occur as a result of point mutations, gene amplifications, chromosomal translocations, the action of certain viruses in the human body and chemical carcinogens (McGregor et al., 2015).

2.2.1 Carcinogenesis

The development of cancer is complicated, multi-staged and multifactorial. Cancer can be caused by internal factors like heredity, immunology and hormonal disorders and external factors like diet and pathogens. Agents that cause cancer are known as carcinogens/ mutagens or oncogenes.

Carcinogenesis/ mutagenesis/ oncogenesis (the molecular process of cancer formation) occurs through four definable stages: initiation, promotion, malignant conversion, and progression (Jackson et al., 2001) as described in Figure 2.1. These stages may progress over several years; thus there is usually a long latent period before cancer appears.

During tumour initiation which is the first phase of tumour development, there is a change in the genetic makeup of normal cells either randomly or by interaction with carcinogens which changes the deoxyribonucleic acid (DNA) of the cells causing irreversible damage (Nowak et al., 2002). Examples of tumour initiating agents are alkylating agents, nitrosamines and aflatoxins. Usually, this initial cell damage does not result in cancer due to the presence of many cellular based DNA damage repair mechanisms. If DNA repair does not occur, the cell then becomes prone to developing cancer.

During tumour promotion, there is selective clonal expansion of initiated cells. This is usually reversible. A carcinogen can be tumour promoting and initiating at the same time. Examples of tumour promoters are cigarette smoke, bile acids and hormones (eg. estrogens).

Malignant conversion involves transformation of benign hyperplastic (pre-neoplastic) cells into malignant cells. Malignant cells can acquire more aggressive attributes like metastases with time.

Figure 2.1: The 4 stages of carcinogenesis. Adopted from Weston A and Harris CC. Multistage Carcinogenesis. In: Kufe DW, Pollock RE, Weichselbaum RR, et al., editors. Holland-Frei Cancer Medicine. 6th Edition. Hamilton (ON): BC Decker; 2003.

Table 2.1: Causes of cancer Cancinogens Sources of carcinogens Type(s) of cancer caused Physical carcinogens Ionising radiations (X-rays, gamma rays) Leukaemia, breast, thyroid, lung Non-Ionising radiations [Ultraviolet (UV) and infrared (IR)] Skin melanoma Physical inactivity, obesity Colorectal, breast, kidney, endometrial, oesophageal Chemical carcinogens Industrial chemicals [vinyl chloride, benzene, benzidine, 2-napthalamine, Liver, leukaemia, lung, skin, bladder, mesothelioma chromium (Cr), nickel (Ni), arsenic (As), cadmium (Cd), asbestos] Chemotherapeutic agents eg. Cyclophosphamide (CPM), etoposide (VP-16), Leukaemia, bladder, endometrial. estrogens, tamoxifen (TMX), Streptozotocin (STZ) Phenacetin, estrogen-progestogen oral contraceptives, hormone-replacement Breast, ovarian therapy (HRT) Tobacco (Benzo [α] pyrene) Lung, oesophageal, laryngeal, mouth, throat, kidney, bladder, pancreas, stomach, cervical High dietary fat intake Breast, colon, prostate Excess consumption of red meat Colorectal Heavy or regular alcohol Oesophageal, stomach, liver, mouth, laryngeal, pharyngeal, Dietary carcinogens intake oropharyngeal, colorectal Nitrosamines Bladder, glioma, stomach, intestinal Aflatoxins Liver Bacterial carcinogens Helicobacter pylori (H. pylori) Stomach, Mucosa-Associated Lymphoid Tissue (MALT) lymphoma Parasitic carcinogens Schistosoma haematobium (S. haematobium) Bladder Opisthorchis viverrini (O. viverrini) Cholangiocarcinoma Clonorchis sinensis (C. sinensis) Liver, cholangiocarcinoma Hepatitis C and B Viruses (HCV and HBV) Liver Human papillomaviruses (HPV) Cervical, anal, oropharyngeal, vulval, penial Herpes Simplex Virus 2 (HSV2) Cervical Epstein- Barr Virus (EBV) Nasopharyngeal carcinoma (NPC), Burkitt‘s Lymphoma, Hodgkin‘s Viral carcinogens lymphoma/ disease (HD). Human Immunodeficiency Virus (HIV) Kaposi‘s sarcoma (KS), invasive cervical, Non-Hodgkin‘s lymphoma (NHL). Human T- Lymphotropic Virus (HTLV) Human T-cell leukaemia or lymphoma. Genetic carcinogens Transformation of proto oncogenes to oncogens (eg. K-Ras, MYC, c-sis, Colon, Burkitt‘s lymphoma, breast, ovarian EGFR, src, HER-2/ neu (erbB-2) Inactivation of tumour suppressor genes (eg. p53 gene, BRCA1 and BRCA2, Breast, colon, stomach, bladder, testicular, ovarian pRb)

2.2.2 Types of cancer Based on cells/ tissues of origin, there are over 200 different known cancers that affect humans (Siegel et al., 2010).

2.2.3 Signs and symptoms of cancer Pain, weakness and fatigue are the most frequent, distressing and feared symptoms experienced by cancer patients (Okuyama et al., 2004; Black et al., 2011). Other signs and symptoms of cancer include wheezing/ shortness of breath (SOB), dysphagia, lymphadenopathy, easy bruising or excessive bleeding, bloating, puffy face, nausea, vomiting, cachexia, chronic cough, constipation, diarrhoea, dyspepsia, erythema, pruritus, frequent fevers or infections, anorexia, confusion, headache, mood swings, cognitive deficits, dry mouth, dyspnoea, insomnia, vertigo and coma (Ferreira et al., 2008; Fearon et al., 2011). 2.2.4 Classification of cancer From a histological standpoint, cancer can be classified into 6 groups; carcinoma, sarcoma, lymphoma, leukemia, myeloma and mixed types (Bruix and Sherman, 2011; Siegel et al., 2010; Modan et al., 2001; Hermann et al., 2007).

2.2.4.1 Carcinomas These are cancers that begin in the skin or tissues that line the internal organs of the body. The two types of carcinomas are adenocarcinoma and squamous cell carcinoma. Carcinomas are the most commonly diagnosed adult cancers (Modan et al., 2001; Hermann et al., 2007).

2.2.4.2 Sarcomas These are cancers of the cells located in connective or supporting tissues of the body such as bone, tendons, cartilage, fat, muscle, nerve and blood vessels. There are two types of sarcomas: soft tissue and bone.

2.2.4.3 Myelomas These are cancers that originate in the plasma cells of the bone marrow and affect the immune system. There are two types of myelomas: isolated plasmacytoma and multiple myeloma.

2.2.4.4 Lymphomas These are cancers which originate in the tissues of the lymphatic system. The two types of lymphomas are Hodgkin‘s and non-Hodgkin‘s.

2.2.4.5 Leukemias These are cancers which originate in blood forming tissues like the bone marrow. Leukemias are the most commonly diagnosed childhood cancers (Modan et al., 2001).

2.2.4.6 Mixed types of cancer This is where two or more cancers occur at the same time in an individual. Example is carcinosarcoma. 2.2.5 Staging of cancer Staging is used by healthcare providers to describe the size and degree of spread of cancer (Edge, 2010). There are 2 main systems for staging cancers. These are Tumour Node Metastasis (TNM) and number systems (Edge, 2010). Staging cancers guide healthcare providers in the estimation of prognosis as well as the treatment options to administer to patients with solid cancers.

2.3 Pharmacological treatments of cancer The aim of pharmacological treatments of cancer may be curative (radical) or palliative (Navolanic and McCubrey, 2005). Pharmacological cancer treatment modalities include medical therapy (eg. cytotoxic chemotherapy), radiotherapy and surgery (von der Maase et al., 2000). Surgery aims to physically cut out the tumour. It is used generally as a curative treatment but can be used as palliative treatment in certain circumstances (Lowery et al., 2012). Radiotherapy uses toxic radiations (eg. gamma rays, X-rays or electrons) to destroy cancer cells and can be administered externally or internally. Surgery or radiotherapy treats cancers that are confined locally (Lowery et al., 2012). Cytotoxic chemotherapy (CTX or CTx) involves the administration of drugs to kill fast dividing cancer cells systemically.

2.3.1 Cytotoxic or cancer chemotherapeutic drugs Cancer chemotherapeutic drugs are currently classified into 9 groups based on their mechanisms of action. These are alkylating agents and related compounds, antimetabolites, antitumour antibiotics, plant alkaloids, hormones and hormone antagonists, radioactive isotopes, monoclonal antibodies (mAbs/ MAB), topoisomerase inhibitors and miscellaneous agents (von der Maase et al., 2000).

2.3.1.1 Alkylating agents and related compounds These drugs act by forming covalent bonds with DNA and hindering DNA replication (von der Maase et al., 2000). They are active against breast, ovarian, testicular, liver, brain, lung, head and neck carcinomas, Hodgkin‘s and non-Hodgkin‘s lymphomas (von der Maase et al., 2000). Alkylating agents and related compounds are further subdivided into classes: i. Nitrogen mustard derivatives: examples include bendamustine, mechlorethamine, cyclophosphamide, chlorambucil, melphalan, and ifosfamide (IFO). ii. Ethylenimines: examples include thiotepa (TSPA) and hexamethylmelamine. iii. Alkyl sulfonates: example is busulfan. iv. Hydrazines and Triazines: examples include altretamine, procarbazine, dacarbazine (DTIC/ DIC) and temozolomide (TMZ). v. Nitrosoureas: examples include carmustine (BCNU), lomustine (CCNU) and streptozotocin. vi. Metal salts (platinum compounds): examples include carboplatin (CB), cisplatin (CP), and oxaliplatin.

2.3.1.2 Antimetabolites Antimetabolites block one or more metabolic pathways involved in DNA synthesis. They are useful in the treatment of childhood Acute Lymphoblastic Leukemia (ALL), chorioepithelioma, acute and chronic myelogenous leukemia (AML and CML), and solid tumours (Peters et al., 2000). Antimetabolites are further sub-divided into classes: i. Folic acid antagonists: examples include methotrexate (Mtx) and penetrexed.

ii. Pyrimidine analogues: examples include 5- Fluorouracil (5-FU), foxuridine, cytarabine (cytosine arabinoside or ARA-C), capecitabine and gemcitabine (dFdCyd). iii. Purine analogues: examples include 6-Mercaptopurine (6-MP), 6-Thioguanine (6- TG), cladribine, fludarabine, and penstostatin.

2.3.1.3 Antitumor (cytotoxic) antibiotics These are antibiotics that impede mammalian cell division by interfering with DNA or ribonucleic acid (RNA) (Mansilla et al., 2010). They are used to treat Kaposi‘s sarcoma, breast, endometrial, ovarian, testicular, thyroid, cervical, colon, rectal, bladder, stomach and lung cancers, Hodgkin‘s lymphoma and Wilm‘s tumour (Mansilla et al., 2010). Antitumor antibiotics are further sub-divided into classes: i. Anthracyclines: examples include doxorubicin (Adriamycin or AC), Liposomal doxorubicin, daunorubicin (DA or DAC), epirubicin (EPR), mitoxantrone (mitozantrone) and idarubicin (IA or IAC). ii. Chromomycins: examples include dactinomycin and plicamycin. iii. Miscellaneous: examples include mitomycin-C and bleomycin.

2.3.1.4 Plant alkaloids Majority of these drugs affect the function of microtubules and impede the formation of the mitotic spindle (Dassonneville et al., 2000). They are used to treat Hodgkin‘s lymphoma, Wilm‘s tumour, Ewing‘s sarcoma, breast, ovarian, cervical, colorectal, testicular and lung cancers (Dassonneville et al., 2000). Classes of drugs under plant alkaloids are: i. Vinca alkaloids: examples include vincristine (oncovin), vinblastine and vinorelbine. ii. Taxanes: examples include paclitaxel (PAX), docetaxel (DOX) and abraxane. iii. Campothecin analogues: examples include topotecan and irinotecan (CPT-11). iv. Podophyllotoxin analogues: examples include etoposide and teniposide (VM-26).

2.3.1.5 Hormones and hormone antagonists Hormones and hormone antagonists are the least toxic of the anticancer drugs (Nord et al., 2003). They are active against breast, endometrial and prostate cancers (Nord et al.,

2003). Hormones act by exerting direct effects on tumour cells (suppression of estrogen receptor levels and alteration of hormonal metabolism) and indirect effects on the endocrine system (suppression of adrenal androgen production). Classes of hormones are: i. Steroids: examples include methylprednisolone, hydrocortisone, prednisone and dexamethasone. ii. Estrogens: examples include diethylstilbestrol (DES) and ethinyl estradiol. iii. Progestins: examples include megestrol acetate and medroxyprogesterone. iv. Androgens: examples include testosterone and danazol. v. Gonadotrophin-releasing hormone (GRH) analogues/ Luteinizing hormone- releasing hormone (LHRH) analogues: examples include goserelin (ZDX), leuprolide and octretide. vi. Aromatase inhibitors: examples include anastrozole, letrozole and exemestane. Hormone antagonists act by suppressing or antagonising hormonal secretion (Tomera et al., 2001). Classes of hormone antagonists are: i. Antiestrogens: examples include tamoxifen, raloxifene, droloxifene and toremifene. ii. Anti-androgens: examples include ketoconazole, bicalutamide, finasteride, flutamide, nilutamide, cyproterone acetate, fluoxymesterone, liazorole and aminoglutethimide.

2.3.1.6 Radioactive isotopes They can be used to treat thyroid tumours (Katsaros and Anagnostopoulou, 2002). Example is radioactive iodine (131I).

2.3.1.7 Monoclonal antibodies Monoclonal antibodies target cancer cells by binding to B cell surface antigens and are useful in treating B-cell lymphomas like non-Hodgkin‘s lymphoma (Weiner et al., 2010). Examples are panitumumab, trastuzumab, gemtuzumab, alemtuzumab, tositumomab, bevacizumab and rituximab.

2.3.1.8 Topoisomerase inhibitors Topoisomerase inhibitors block the effects of the topoisomerase (topoisomerase I and II) enzymes which control DNA topology and are vital for the integrity of the genetic material during transcription, replication and recombination processes (Pommier, 2006). Examples of topoisomerase I inhibitors are irinothecan and topothecan. Examples of topoisomerase II inhibitors are doxorubicin, etoposide, irinothecan and mitoxantrone.

2.3.1.9 Miscellaneous agents Examples of miscellaneous agents are suramin, L-asparaginase, amsacrine, crisantaspase and mitotane. 2.3.2 Combination chemotherapy Chemotherapeutic drugs are often combined in groups of two, three, or more, an act called combination chemotherapy. The rationale for combination chemotherapy is synergism, reduced cancer resistance and reduced side effect profiles (von der Maase et al., 2000). 2.3.3 Toxic effects of chemotherapeutic agents Toxic effects of chemotherapeutic drugs include: i. Bone marrow: depression (myelosuppression) resulting in granulocytopenia, agranulocytosis, thrombocytopenia and aplastic anaemia. ii. Lymphoreticular tissue: lymphocytopenia and inhibition of lymphocyte function resulting in the suppression of cell mediated as well as humoral immunity. iii. Respiratory system: severe shortness of breath, wheezing, tachypnoea, dsypnoea, cough, haemoptysis, chest pain. iv. Gastrointestinal tract (GIT/ GI): mucositis, stomatitis, numbness/ tingling or sores in and around the mouth, diarrhoea and/ constipation, loss of appetite, haematemesis, nausea and/ vomiting, clay-colored stools, abdominal pain. v. Cardiovascular system (CVS): tachycardia, bradycardia, palpitations, intermittent claudication. vi. Central nervous system (CNS): headache, fainting, confusion. vii. Musculoskeletal system: myalgia, arthralgia, back pain, joint pain. viii. Urogenital system: heamaturia, oliguria, dark urine.

ix. Skin: loss of hair (alopecia), pale skin, purple or red pinpoint spots under the skin, injection-site burning and stinging (ISBS), hives, jaundice, darkening of the skin, pruritus. x. Reproductive system: oligospermia, amenorrhoea, menopause, post coital bleeding. xi. Foetus: foetal abortion, foetal death and teratogenesis in pregnant women. xii. Carcinogenicity: secondary cancers especially leukaemias and lymphomas may appear many years after using chemotherapeutic drugs. xiii. Hyperuricaemia: can cause an increase in uric acid levels leading to gout. xiv. Bleeding: easy and frequent bruising, unusual bleeding (eg. nose, mouth, vagina or rectum). xv. Individual drugs may cause specific adverse effects in the body. For example cystitis and alopecia may be caused by cyclophosphamide; neuropathy may be caused by vincristine and cardiomyopathy by doxorubicin.

2.3.4 Combined cancer treatment modalities For some cancers, the best approach to effective management is a combination of radiotherapy, surgery and chemotherapy as neoadjuvant, adjuvant or concurrent chemotherapy (DeAngelis et al., 2002). Neoadjuvant chemotherapy involves administering anticancer treatments (eg. radiotherapy or chemotherapy) before the primary treatment (eg. surgery) to maximise therapy. Adjuvant chemotherapy involves administering anticancer treatments (eg. radiotherapy or chemotherapy) after the primary treatment (eg. surgery) with the aim of maximising therapy. Concurrent chemotherapy involves administering anticancer treatments (eg. radiotherapy or chemotherapy) at the same time as the primary treatment (eg. surgery). The stage and type of cancer often determines whether single or combined therapy will be helpful.

2.3.5 Factors influencing cancer treatment options Factors influencing cancer treatment options include: tumour, patient and treatment. Patient factors: This include performance status (PS), co-morbidities, gender, age and patient‘s preference. Perfomance status is a measure of the functional capacity of a patient including his/ her ability to carry out certain Activities of Daily Living (ADLs) like bathing and using the toilet without the help of other people. Perfomance status can be assessed by the tenets 13

of the Eastern Cooperative Oncology Group (ECOG) or the Karnofsky scales (Gunn et al., 2013). The Eastern Cooperative Oncology Group performance status assessment uses 0-5 to describe the functional capacity of a cancer patient as shown in Table 2.2. Assessment of the perfomance status of a patient‘s can be used to determine appropriate treatment modalities (Gunn et al., 2013). Tumour factors include the location, stage and spread of the cancer. Treatment factors include availability of treatments and their side effect profiles. Table 2.2: Description of the grades of ECOG performance status of cancer patients* Grade Eastern Cooperative Oncology Group performance status 0 Fully active and capable of carrying out all pre-disease performance without restrictions 1 Ambulatory, able to do all self-care, but restricted in physically strenuous activities 2 Ambulatory, capable of all self-care, limited in physically strenuous activities but up and about >50% of waking hours in a day 3 Capable of limited self-care, restricted in physically strenuous activities, up and about < 50% of waking hours in a day 4 Completely disabled and totally bed or chair bound 5 Dead *Oken MM, Creech RH, Tormey DC et al. (1982) Toxicity and response criteria of the Eastern Coorperative Ocology Group. American Journal of Clinical Oncology 5(6); 649-655.

2.4 Cancer pain The International Association for the Study of Pain (IASP) defines pain as ―an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage‖ (Merskey, 1994; Leila et al., 2006; Jensen et al., 2011). Pain is an obvious threat to health as described by the World Health Organization (WHO) (Bircher, 2005) and has been declared by the American Pain Society (APS) as the 5th vital sign (Ballout et al., 2011). Cancer pain is estimated to occur in about 20-50% of patients during diagnosis and in approximately 80% as the disease progresses (Prommer, 2015 and Ogboli-Nwasor et al., 2013). Cancer pain can however be effectively controlled in 80-90% of patients (Reyes-Gibby et al., 2006).

2.4.1 Causes of cancer pain Cancer pain is mostly caused by: i. Direct cancer growth into human tissues (eg. pathologic fractures) and/ or metastases (eg. bone and soft tissue metastases). ii. Diagnostic procedures [eg. post-biopsy pain and post lumber puncture headache]. iii. Antineoplastic therapies: eg. adverse effects of radiotherapy, chemotherapy, immunotherapy, hormonal therapy, post-procedural and post-surgical pain. iv. Co- existing non-malignant conditions eg. constipation.

2.4.2 Types of cancer pain Cancer pain can be categorised based on histological standpoint or by onset and duration.

2.4.2.1 From histological standpoint Cancer pain can be nociceptive or neuropathic (Portenoy, 1992). Nociceptive pain is caused by damage to body tissues and can be either somatic (bones, joints, connective tissues and muscles) or visceral (internal organs eg. heart and liver). Somatic nociceptive pain is commonly well localized, constant and can be described as ―aching‖ or ―throbbing‖ in quality (Portenoy, 1992). Visceral nociceptive (soft tissue) pain tends to be episodic and poorly localized and can be described as ―cramping‖ or ―gnawing‖ in quality (Portenoy, 1992). Visceral pain is often referred to somatic sites in the body (that is the pain is perceived to be in a location that is not the source of the pain). The referral of pain however often correlates with visceral pain intensity (Ly et al., 2013). Nociceptive pain is commonly time limited (that is when tissue damage heals, the pain typically resolves) and tends to respond well to opioid treatment. Examples of nociceptive cancer pain syndromes are metastatic bone pain and surgical incision pain. Neuropathic pain is commonly caused by injury to the nervous system as a result of compression or adverse effects of cancer treatments. Neuropathic cancer pain is usually severe, chronic and can be described as ―burning‖ or ―tingling‖ in quality (Caraceni, 2001). Neuropathic pain syndromes are often associated with referred pain, allodynia (that is pain induced by non-painful stimulus), hyperpathia (that is exaggerated pain response to nociceptive stimuli) or dysesthesia (that is unpleasant, abnormal sensation in an area of neurologic deficit). Neuropathic pain tends to respond poorly to opioids

but responds very well to anticonvulsants (eg. gabapentin) and antidepressants (eg. imipramine). Examples of neuropathic cancer pain syndromes are phantom pain and post chemotherapy pain. Neuropathic and nociceptive pain can be experienced singly or concurrently in a cancer patient (De Oliveira et al., 2014; Caraceni, 2001).

2.4.2.2 By onset and duration Acute cancer pain syndromes usually have a sudden onset and are usually iatrogenic (i.e. due to medical tests or treatments) but could also be due to disease-related complications. Acute cancer pain syndromes are often subject to sympathetic output (fight or flight response resulting in diaphoresis, hypertension and tachycardia), associated with overt pain behaviours (eg. grimacing, splinting and moaning) and lasts less than 3 months. It can be subacute (that is increase in intensity over time) or episodic (occur intermittently) in nature (Caraceni, 2001). Acute cancer pain syndromes include mucositis, palmar-plantar erythrodysesthesia (PPE or hand-foot syndrome), post-biopsy pain and post lumber puncture headache. Conversely, chronic cancer pain syndromes have a less distinct onset with a prolonged and alternating course. Chronic cancer pain syndromes are commonly due to direct effects of malignancy but may also be due to antineoplastic treatments which last for more than 3 months. Chronic cancer pain can be continuous or intermittent (Caraceni, 2001). Unrelieved chronic cancer pain can lead to depression, anxiety, anorexia and insomnia. Chronic cancer pain syndromes include phantom limb pain and chronic post- surgical pain [eg. thoracotomy, mastectomy and neck dissection].

2.5 Assessment of cancer pain Routine comprehensive assessment of pain in cancer patients is an essential step toward successful pain management (Ogboli-Nwasor et al., 2013). Comprehensive cancer pain assessment should cover all aspects of the cancer pain experience. Pain intensity usually depends on the cancer type, stage, location and side effects of anticancer treatments (Cook et al., 2015; Droz and Howard, 2011; McCaffery et al., 2000). The temporal pattern of cancer pain can be continuous (persistent/ background) or intermittent. Background cancer pain is usually chronic and can last all day. Intermittent cancer pain can be incident (that is pain which is linked to a known precipitating factor), non-incident (that is breakthrough cancer pain or spontaneous

cancer pain which is not linked to any known precipitating factor) and end-of dose failure (that is pain which occurs before a scheduled opioid dose). Crescendo or altered reported pain patterns in addition to the usual underlying background pain is usually indicative of disease advancement, treatment complications or cancer recurrence (Portenoy et al., 2010). Bio psychosocial factors that could alter the perception of pain and coping strategies include past experiences, cultural and religious beliefs, psychological factors (such as anxiety and depression) and social history/ issues (Cuff et al., 2014). Physiological pain indicators including increased: heart rate, blood pressure and respiratory rate may occur in sudden or severe transient pain which disappears as the body seeks to maintain homeostasis. Physiological pain indicators may be masked by medical conditions (eg. hypothyroidism) or treatments (eg. beta- blockers) which makes them neither sensitive nor specific. Because of this, the American Pain Society cautions against the use of physiological pain indicators in pain assessment (Ballout et al., 2011). The point in time at which pain assessments are carried out also needs consideration. Cancer pain should be assessed and recorded when the patient presents with pain (that is baseline pain), after pain interventions, at rest, during movement (that is dynamic pain) and while undergoing activity (eg. physiotherapy). Autonomic signs, psychosocial and psychiatric factors as well as multiple pain sites should be considered when assessing cancer pain. Challenges of cancer pain assessment include: multiple cancer pain mechanisms, lack of a universally accepted cancer pain classification system, lack of objective testing modalities, time constraints of staff and individual differences in cancer pain sensitivity (Chapman, 2012).

2.5.1 Cancer pain assessment tools Two main principles apply in the design of pain assessment tools: patient self-report and proxy (observational) assessments. Patient self-report is the gold standard (Kim et al., 2012).

2.5.1.1 Patient self-report pain assessment tools Patient self-report pain assessment tools are currently preferred for cognitive and verbally competent patients. There are 2 types of patient self-report pain assessment

tools: one-dimensional and multidimensional (Caffo et al., 2011; Williams et al., 2013). Patient self-report pain assessment tools are also known as Patient Reported Outcomes (PROs) or Patient Reported Outcome Measures (PROMs) (Clark et al., 2014).

2.5.1.1.1 One-dimensional pain assessment tools i. Visual Analogue Scales (VAS) A Visual Analogue Scale is a 10 centimeter (cm) [100 millimeters (mm)] straight horizontal (HVAS) or vertical (VVAS) line that represents the intensity of pain to be measured in a patient. This scale is conventionally marked at each end with labels that suggest the range of pain to be measured. The left end of the straight line usually denotes ―no pain‖ while the right end represents excruciating pain captured as ―worst possible pain‖. Patients are asked to place a mark on a spot on the line which best represents the intensity of their pain. A ruler can then be applied to measure the distance from the ―no pain‖ spot to the marked spot to give a direct indication of the patient‘s pain intensity. Limitations of VAS include the fact that it requires that the patient‘s sensory experience of pain be translated into a spot on the straight line and the fact that the patient should have an intact motor and coordination system.

Figure 2.2: Visual Analogue Scale (VAS) for cancer pain assessment in adults.

ii. Verbal Descriptor Scales (VDS)

A Verbal Descriptor Scale, also known as a Verbal Rating Scale (VRS) applies verbal pain descriptors such as ―no pain‖, ―mild pain‖, ―moderate pain‖ ―severe pain‖, ―very severe pain‖ and ―worst possible pain‖ to access the patient‘s pain. It may include more or less verbal pain descriptors. It allows patients to assess their pain by choosing a verbal pain descriptor which best describes the intensity of their pain. Although patients prefer VDS to VAS (Caffo et al., 2011), one important limitation of VDS is the fact that it should be validated for the patient cohort so that verbal pain descriptors will be meaningful to the patient.

Figure 2.3: Verbal Descriptor Scale (VDS) for cancer pain assessment in adults.

iii. Numerical Rating Scales (NRS)

A Numerical Rating Scale can be a horizontal (HNRS) or vertical (VNRS) line with equal divisions ranging from 0–10 with 0 anchored on the extreme left representing ―no pain‖ and 10 on the extreme right representing ―worst possible pain‖. Numerical Rating Scales can be 11-point, 5-point etc. and they work by asking the patient to choose a number on the scale that represents his/ her pain intensity. Numerical Rating Scales have good psychometric properties, are easy to understand and score, devoid of ambiguity and are best to use for cross-linguistic pain assessments (Castel et al., 2007). These attributes make NRS preferable to patients over VAS or VDS (Castel et al., 2007).

Figure 2.4: Numerical Rating Scales (NRS) for cancer pain assessment in adults.

iv. Pictorial Rating Scales

These scales are commonly used for self-report pain assessments in children (Franck et al., 2000) . The child is asked to indicate by pointing to a face which best describes his/ her pain or discomfort level. Examples of pictorial rating scales are the Faces Pain Scale- Revised (FPS-R) and the Wong-Baker FACES Pain Rating Scale (Garra et al., 2010; Hicks et al., 2001).

Figure 2.5: Faces Pain Scale- Revised (FPS-R) for cancer pain assessment in children.

Figure 2.6: Wong-Baker FACES Pain Rating Scale for cancer pain assessment in children.

2.5.1.1.2 Multidimensional cancer pain assessment tools They are mostly questionnaires which utilize pain rating scales to assess multiple facets of pain. Examples of multidimensional cancer pain assessment tools are the McGill Pain Questionnaire (MPQ), Short- Form McGill Pain Questionnaire (SF- MPQ) and the Brief Pain Inventory.

i. The Brief Pain Inventory (BPI)

The Brief Pain Inventory, formerly called the Wisconsin Brief Pain Questionnaire was designed by the Pain Research Group of the University of Wisconsin in Madison, USA in collaboration with the WHO Centre for Symptom Evaluation in Cancer Care (Atkinson et al., 2010; Atkinson et al., 2012). The Brief Pain Inventory is one of the most accepted and frequently used PROM for assessing multidimensional pain including cancer pain in both clinical and research settings (Ballout et al., 2011; Caffo et al., 2011; Hadi et al., 2008). Although originally developed to access cancer pain, the BPI has also been used in the accessment of pain in patients with AIDS, osteoarthritis

and other chronic non-cancer pain syndromes across different cultures (Aisyaturridha et al., 2006; Atkinson et al., 2012; Ballout et al., 2011; Black et al., 2011).

The Brief Pain Inventory is an easily understood and easily scored PRO which can be self/ interviewer/ telephone - administered (Aisyaturridha et al., 2006; Atkinson et al., 2012; Caffo et al., 2011). It is clearly written, not physically invasive to the patient, easily accessible and free online, requires little equipment and no training. The Brief Pain Inventory is available in two forms; short (BPI-SF) and long (BPI-LF) both of which can be used in clinical and research settings (Ballout et al., 2011). The Brief Pain Inventory is organized to assess 5 main aspects of cancer pain: history and quality, pain intensity index, pain interference with daily functioning, pain location and the effects of pain treatments.

2.6 Management of cancer pain Cancer pain can be managed pharmacologically and non-pharmacologically.

2.6.1 Pharmacological cancer pain management There are several practice guidelines intended to facilitate and standardize pharmacological management of cancer pain and advice physicians worldwide on how to achieve optimum pain control (Benedetti et al., 2009). Cancer pain can be managed pharmacologically with analgesics (WHO analgesic ladder approach), anticancer therapies, nerve blocks and psychological techniques (Knotkova and Pappagallo, 2007; Liang et al., 2013).

2.6.1.1 WHO analgesic ladder It has been well documented that successful use of the WHO analgesic ladder approach to cancer pain management can provide effective analgesia to 80-90% of patients (Prommer, 2015; Arcidiacono et al., 2011; Ogboli-Nwasor et al., 2013). The WHO analgesic/ pain ladder approach is considered the cornerstone of pharmacological cancer pain management (Vargas-Schaffer, 2010; Harding et al., 2010; Marinangeli et al., 2004; Dalton and Youngblood, 2000). The WHO analgesic ladder approach to cancer pain management recommends a three-step (step 1, step 2 and step 3) approach to pharmacological cancer pain management based on the patient‘s self-reported pain intensity (Dalton and Youngblood, 2000). The guideline also recommends that

analgesic treatment should be patient-based (individualized) depending on pain patterns, preferably administered by mouth, at regular intervals [―around the clock‖ (ATC) dosing- every 4, 6, 12 or 24 hours rather than on demand] and with attention to details to ensure continuous cancer pain relief (Vargas-Schaffer, 2010). The guideline also recognizes the occurrence of breakthrough cancer pain and directs the administration of immediate rescue doses (strong opioids, adjuvants and non-opioids) (Dalton and Youngblood, 2000).

i. Step 1

This involves the utilization of non-opioid analgesics eg. paracetamol (acetaminophen) and Non-Steroidal Anti-inflammatory Drugs (NSAIDs) and/ or adjuvants for the pharmacological treatment of mild to moderate cancer pain (Dalton and Youngblood, 2000).

ii. Step 2 If cancer pain persists or increases in intensity, weak opioids should be added to existing non-opioid regimen. Adjuvant analgesics can be added to the treatment regimen (Dalton and Youngblood, 2000). Examples of weak opioids are codeine, tramadol and dihydrocodeine (DF118). iii. Step 3

If cancer pain worsens, weak opioids should be replaced with strong opioids while continuing existing non-opioid regimen (Marinangeli et al., 2004; Paley et al., 2015). Adjuvant analgesics can be added. If the initial presentation is severe pain such as post- surgical pain, a strong opioid therapy should be started immediately in combination with a non-opioid analgesic (Dalton and Youngblood, 2000).

2.6.1.2 Analgesics Analgesics comprise non-opioids, opioids and adjuvants.

2.6.1.2.1 Non-opioid analgesics i. Paracetamol: (also known as acetaminophen) is centrally-acting and is widely used in prescription and over-the-counter (OTC) medications to relieve fever and treat

mild to moderate cancer pain (Dalton and Youngblood, 2000). It can be administered per oral (PO), intravenously (IV), intramuscularly (IM) and rectally (PR). In combination with an opioid, IV paracetamol can also be used in the treatment of severe pain such as cancer pain and post-surgical pain. Although paracetamol is generally safe at recommended doses, high doses can cause adverse effects like liver and kidney failures (Prommer, 2015). ii. Non-Steroidal Anti-Inflammatory Drugs: these drugs have anti-inflammatory, anti-pyretic and analgesic effects and can be used in cancer pain management. They act by reducing the production of pro-inflammatory prostaglandins (PG) and thromboxanes through cyclooxygenase (COX) inhibition (Dalton and Youngblood, 2000; Duncan et al., 2012). They are categorised based on their capability to hinder either the COX-1 (constitutive) or COX-2 (inducible) enzyme isoforms or both (Prommer, 2015).COX-1 enzyme blockade is responsible for most adverse effects linked with NSAIDs. COX-2 enzyme blockade is responsible for the analgesic activities of NSAIDs. Traditional NSAIDs inhibit both COX-1 and COX-2 enzymes. Examples of COX-1 selective inhibitors are indomethacin, low-dose aspirin and ketorolac. Examples of COX-2 selective inhibitors are celecoxib, rofecoxib and entoricoxib. Examples of non-selective COX inhibitors are aspirin (ASA), fenamates, piroxicam, diflunisal, ibuprofen, diclofenac, meloxicam and naproxen. iii. NSAIDs can be administered via PO, IV, IM, topical, PR, opthalmologic, intranasal, transdermal and vaginal routes (Prommer, 2015). Side effects of NSAIDs include oedema, high BP, heart failure, GIT disturbances (dyspepsia, nausea and vomiting), skin reactions (mild rashes, urticaria, and photosensitivity reactions), bleeding, bronchospasm, renal toxicity, delayed bone healing, increased thrombotic events, platelet dysfunction, dizziness and headache.

2.6.1.2.2 Adjuvant analgesics Adjuvant analgesics, also known as co-analgesics are drugs designed primarily for indications apart from pain but may have analgesic properties in certain painful conditions. Although they can be administered alone, they are usually co-administered with a primary analgesic on the various steps of the WHO analgesic ladder. This is done

to enhance analgesia and reduce possible side effects (Lussier et al., 2004; Prommer, 2015). In choosing the right adjuvant analgesic, it is essential to infer the pathophysiology of the cancer pain syndrome (Lussier et al., 2004). Table 2.3: Classes of adjuvant analgesics used for cancer pain management* Classification based on clinical Adjuvant analgesics use Multi-purpose analgesics Tricyclic antidepressants (TCA) (example amitriptyline), corticosteroids (example dexamethasone), α-2 adrenergic agonists (example clonidine), neuroleptics (example olanzapine), bupropion, capsaicin, cannabinoids (example dronabinol), lidocaine. Neuropathic pain specific Anticonvulsants (examples are gabapentin and pregabalin), local anaesthetics (example lidocaine), N-Methyl-D-Aspartate (NMDA) receptor antagonists (example ketamine), psychostimulants (example methylphenindate), muscle relaxants (example baclofen). Bone pain specific Calcitonin, bisphosphonates and radiopharmaceuticals (example Strontium-89). Bowel obstruction specific Anticholinergics (example benztropine), somatostatin analogues (example octreotide) and corticosteroids (example prednisolone). *Lussier D, Huskey AG and Portenoy RK (2004) Adjuvant analgesics in cancer pain management. The oncologist 9(5); 571-591.

2.6.1.2.3 Opioid analgesics (narcotics) Opioids can be endogenous (eg. endorphins) or exogenous (Prommer, 2015). Exogenous opioids are synthetically made. Examples of strong exogenous opioids are morphine, diamorphine, oxycodone, oxymorphone, fentanyl, hydromorphone, pethidine, levorphanol, buprenorphine and methadone (Marinangeli et al., 2004). Opioids act by binding to their receptors; mu (m or µ), delta (d) and kappa (k) located in the brain, spinal cord, and the GIT (Prommer, 2015). These receptors which are G- protein coupled mediate the release of dopamine in the pain processing regions of the brain (thalamus, brainstem and spinal cord) causing less pain (Prommer, 2015). When dopamine release occurs in the reward pathway regions of the brain (such as the ventral tegmental area, nucleus accumbens and prefrontal cortex), it causes a calming effect and euphoria (emotional ―high‖) (Marinangeli et al., 2004; Hollingsworth et al., 2017). Opioids can be used to manage both acute and chronic cancer pain. In acute cancer pain management, short-acting opioids should be given at the lowest effective dose for a few

days and the dose titrated slowly as needed. In chronic cancer pain management, non- opioid approaches such as exercise, biofeedback and non-opioid analgesics are the gold standard although opioids can be administered periodically depending on pain intensity (Prommer, 2015). Opioids can be administered in recommended or titrated doses as long as side effects are tolerable to the patient (especially opioid-tolerant patients) because they do not have a ceiling effect. They can be administered via PO, IM, IV, subcutaneous (SC), sublingual, intranasal, epidural, intrathecal, PR and transdermal routes. Over the past years, morphine has been deemed the opioid of choice for the management of moderate to severe cancer pain. Some recommendations have however endorsed oxycodone and hydromorphone as first line opioids for managing moderate to severe cancer pain (Dekel et al., 2014; Yu et al., 2014). Side effects of opioids include tolerance, dependence, addiction, sedation, dry mouth, euphoria, constipation, nausea and/ or vomiting, confusion, drowsiness, pruritus, myoclonus, respiratory and cardiac depression (Ahmedzai et al., 2015; Andreassen et al., 2012, Klepstad et al., 2002; Arcidiacono et al., 2011; Breitbart et al., 1998).

2.6.2 Non-pharmacological cancer pain management strategies Complementary cancer pain management strategies usually complement pharmacological cancer pain management approaches (Crew et al., 2010). Examples of complementary cancer pain management strategies are massage therapy, acupuncture, superficial heat and cold therapy, ultrasound, hydrotherapy, yoga, music, relaxation, physiotherapy, psychotherapy, gymnastics, imagery and hypnosis (Crew et al., 2010; Batalha and Mota, 2013; Ogboli-Nwasor et al., 2013 Grosen et al., 2012).

2.7 Pain Management Index (PMI) of patients Pain Management Index is a simple index linking patient‘s worst reported pain level to the strength of prescribed analgesic(s) (Deandrea et al., 2008). Pain management is deemed adequate if there is consonance between the patient‘s subjective self-reported pain intensity and prescribed analgesic(s) and described as follows: PMI < 0 = inadequate pain management. PMI 0 = optimum pain management. PMI > 0 = over treatment of pain (Donovan et al., 2008; Deandrea et al., 2008).

2.8 Challenges to effective cancer pain management Poor or inadequate cancer pain management has been identified by the WHO as a global health challenge of the highest priority (Reyes-Gibby et al., 2006). Challenges to adequate cancer pain management have been identified and classified by the Agency for Health Care Policy and Research (AHCPR) into 3 categories: system, professional and patient factors (Ogboli-Nwasor et al., 2013). System factors include low consideration given to cancer pain treatment and the legal and regulatory obstacles surrounding the use of opioids for cancer pain management. Professional factors include failure of comprehensive cancer pain assessments, inadequate knowledge of health care workers in cancer pain management (poor knowledge of opioid pharmacology and failure to use adjuvants). Patient factors include patient‘s reluctance to report cancer pain and non- adherence to analgesic regimens (Reyes-Gibby et al., 2006; Ogboli-Nwasor et al., 2013).

2.9 Effects of cancer pain on the quality of life of patients The WHO Quality of Life (WHOQoL) Group defines quality of life as an ―individual‘s perception of his/her position in life in the context of the cultural and value systems which exist where they live in relation to their goals, expectations, standards and concerns‖ (Castro et al., 2007; Nedjat et al., 2008; Ohaeri et al., 2007; Awadalla et al., 2006). Health-related quality of life (HRQoL) narrows quality of life assessments to aspects relevant to the health of patients by applying holistic, interactive and patient- centred strategies to enhance the patients‘ quality of life (Ginieri-Coccossis et al., 2009; Awasthi et al., 2012). Cancer pain can significantly affect the quality of life of sufferers which necessitates its assessments.

2.9.1 Quality of life assessment tools Examples of quality of life assessment tools are the World Health Organization Quality of Life (WHOQoL) instruments and the Medical Outcomes Study Short-Form 36-Item (MOS-SF-36).

2.9.1.1 World Health Organization Quality of Life (WHOQoL) instruments The WHOQoL group; instituted in 1991 had the aim of developing an international cross-culturally comparable quality of life assessment instrument. Two quality of life

instruments; World Health Organization Quality of Life- 100 (WHOQoL-100) and World Health Organization Quality of Life- Brief version (WHOQoL-Bref) were developed as a result (Castro et al., 2007; Jaracz et al., 2006; Nedjat et al., 2008; Asnani et al., 2009).

2.9.1.1.1 The World Health Organization Quality of Life- Brief version The WHOQoL-Bref is an abbreviated version of the WHOQoL-100 which contains 26- items divided into 4 domains (DOM) with related facets. The WHOQoL-Bref is popular because of its succintness which reduces patients‘ response burden and it is ideal for use in clinical settings (Krägeloh et al., 2011). The WHOQoL-Bref is also useful where facet-level details are unnecessary such as large epidemiological surveys and some clinical trials.

2.9.1.1.1.1 Domains/ facets of the World Health Organization Quality of Life- Brief version The WHOQoL group defines a domain (or dimension) as a broad grouping of related facets (sub-domains) (Espinoza et al., 2011) and a facet as a behaviour, a state of being, a capacity or potential, or a subjective perception or experience (Chiu et al., 2006). The 4 domains of the WHOQoL-Bref with related facets are: physical health (DOM 1) - 7 facets; psychological health (DOM 2) - 6 facets; social relationships (DOM 3) - 3 facets and environmental health (DOM 4) - 8 facets.

2.10 Conceptual framework of the study The various processes involved in the study have been briefly outlined successively in Figure 2.7 as literature on cancer pain, research design and recruitment of study group, effects of pain on the quality of life of cancer patients, data collection and analysis, results and implications for research.

Literature on Cancer Pain Research Design and Recruitment of Study group  Causes  Proposal drafting  Types  Ethical considerations and approval  Assessment  Data test Sampling and Recruitment of  Management Participants  Pain Management Index

Effects of Pain on the quality of life of cancer patients  Identify Quality of life(QoL) changes due to cancer pain  Use QoL assessment tools

Data Analysis  Data input to Microsoft Excel 2013 Software and data analysis with SPSS© V.24. RESULTS  Generate Descriptive analysis, parametric and non- parametric tests

Study Implications

Providers – Clinical Care Public Team e.g Doctors, Pharmacists e.t.c

Policy makers

 Funding and Support for relevant research which includes randomized control trials, epidemiological studies.  Increase in cancer assessment tools and diagnostics  Mandatory development of cancer registry at hospitals to facilitate research  Combined team effort and support in the health care setting

Figure 2.7 Conceptual framework of the study

CHAPTER THREE 3.0 SOCIODEMOGRAPHICS, CLINICAL CHARACTERISTICS AND DRUG HISTORY OF PATIENTS

3.1 Introduction The incidence of cancer is usually because of a constellation of factors including patient factors, environmental factors and patient‘s lifestyle. Patient factors include age and genetic factors, environmental factors include occupation risks and pollution while lifestyle factors include smoking, alcohol use, physical inactivity, obesity and diet.

Age may reflect the length of exposure of patients to carcinogens owing to lifestyle and environmental factors and can lead to the development of some cancer syndromes (Ahlner-Elmqvist et al., 2008). Gender can influence the clinical course of cancer diagnosis and affect treatment outcomes by affecting health seeking behaviour and access to health information (Consedine et al., 2004; Million Women Study Collaborators, 2003). Family history of cancer and racial predilections of patients can affect the incidence of certain hereditary cancer syndromes such as breast, colorectal and ovarian (Antoniou et al., 2003; Kelsey et al., 1993; La Vecchia et al., 1992).

Heavy or regular alcohol consumption, tobacco use, environmental tobacco smoke (ETS), carcinogenic diets and physical inactivity can lead to the development of certain cancers (Benedetti et al., 2009; Khan et al., 2008; Anast et al., 2005; Renehan et al., 2008; Garrouste-Orgeas et al., 2004). Sex-related infections like HIV/ Acquired Immune Deficiency Syndrome (AIDS) are also indicated in the development of some cancers (Patel et al., 2008).

Furthermore, exposure to non-ionizing radiations, occupational carcinogens and environmental pollutants can cause certain cancers (Aghillinejad et al., 2016).

This study therefore seeks to establish whether socio-demographic and clinical characteristics of cancer outpatients directly affect their incidence of cancer and to document cancer pain management approaches at the Komfo Anokye Teaching Hospital.

3.2 Methodology

3.2.1 Ethical clearance for the study In order to undertake the study, the research topic was registered at the Research and Development Unit, Komfo Anokye Teaching Hospital (KATH) and approval subsequently sought from the Oncology Directorate, KATH. Ethical clearance was obtained from the Committee on Human Research, Publications and Ethics (CHRPE) of the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi-Ghana (CHRPE/RC/012/15) (Appendix 1). The following documents were reviewed by the committee after which approval was given: i. A certificate of registration from the Research and Development Unit, KATH (Appendix 2) ii. A notification letter from study site (Appendix 3) iii. A completed CHRPE application form (Appendix 4) iv. Patient information leaflet and consent form (Appendix 5) v. Research protocol (Appendix 6) vi. Data collection forms (Appendices 7, 8 and 9)

3.2.2 Study setting This study was carried out at the Oncology Directorate; a comprehensive cancer treatment center of the Komfo Anokye Teaching Hospital in Kumasi; the Regional Capital of the Ashanti Region of Ghana (6:41:46.78N, 1:37:44.79W). KATH is a 1200- bed capacity Teaching Hospital. It is the second largest hospital in Ghana after the Korle-Bu Teaching Hospital (KBTH) in Accra, Ghana. Due to the geographical location coupled with the extent of commercial activities in Kumasi, KATH serves patients from the Northern, Upper East, Upper West, Brong Ahafo, Western, Central, Eastern and Volta Regions of Ghana as well as other countries in Sub-Saharan Africa. It is considered as one of the best hospitals in Ghana and Sub-Saharan Africa in terms of provision of cancer care.

3.2.3 Study design This was a quantitative study based on a descriptive cross-sectional design. The descriptive cross-sectional design approach is relatively inexpensive, a lot of outcomes

can be assessed and it offers the flexibility of obtaining relevant primary data in a relatively short time which applied to the objectives of this study.

3.2.4 Sources of data The sources of data were the medical folders of consenting cancer outpatients who met the study‘s eligibility criteria and the Hospital Administration Management Systems (HAMS).

3.2.5 Sampling procedure and sample size determination The total number of outpatients who were newly diagnosed with different types of cancer in 2014 at the Oncology Directorate, KATH was seven hundred and fourty seven (747). Out of this number (747), a representative sample was calculated using the Yamane‘s formula for sample size determination as described by Singh and Masuku (2014). The Yamane‘s formula for sample size determination is given as:

Where n = sample size, N = total patient population size and e = sampling error.

Given that the total number of patients who reported to the Oncology Directorate, KATH in 2014 with different cancers was 747, the Yamane‘s formula for sample size determination becomes: n=747/1+747(0.05)2 n=260.5 ~ 261

The estimated number of medical folders of these patients for sampling was therefore two hundred and sixty one (261). A total of two hundred and four (204) patient medical folders representing a response rate of 78% were however reviewed using a self-designed Epi-info version 7 questionnaire. The difference between the estimated number of patient medical folders and the actual number reviewed for this study was due to factors such as lack of verbal informed consent by patients, missing and incomplete medical folders and poor information documentation. 31

A non-probabilistic sampling procedure was adopted for this study because of the infinite nature of the study sample and the absence of a well-defined sampling frame for probabilistic sampling.

3.2.6 Data collection instrument A structured questionnaire designed using Epi-info version 7 (CDC, Atlanta, Georgia, USA) (Appendix 7) was used to document relevant information from patients‘ medical folders and the Hospital Administration Management Systems.

3.2.7 Pretesting of data collection instrument The designed data collection instrument was pretested on a convenient sample of 15 medical folders not included in the study which met the eligibility criteria for the study one month prior to the commencement of the review process to ascertain the feasibility of the tool. The findings from the pre-testing of the questionnaire enabled modifications, deletion of ambiguous, irrelevant information and addition of missing data items.

3.2.8 Data collection procedure The medical folders of consenting cancer patients visiting the Oncology Directorate, KATH for treatment were reviewed from 2nd January to 30th December, 2015 and key socio-demographic and clinical characteristic variables were elucidated with the aid of the self-designed questionnaire. The current drugs prescribed for patients were also elucidated from medical folders and the Hospital Administration Management Systems. Challenges encountered during the data collection process included missing folders, incomplete folders, poor information documentation and poor folder identification numbers.

3.2.8.1 Social history of patients The social history variables recorded from patient‘s medical folders were age at diagnosis, gender, religion, marital status, employment status, occupation, region of residence, directorate of referral to the Oncology Directorate- KATH, baseline performance status, weight, history of: alcohol consumption, tobacco use and

recreational drug use (eg. cocaine, cannabis), predisposing diets and sexual habits/ preference. In addition to these variables, for female patients their obstetric history comprising parity, gravidity, abortus status, age at first live birth, age at menarche, last menstrual period (LMP), menopausal status and age at menopause in postmenopausal women were recorded from medical folders.

i. Occupation

The occupations of patients were recorded from medical folders and classified according to the International Standard Classification of Occupations 2008 (ISCO-08) classification method as described by Ganzeboom and Treiman (2010).

ii. Baseline performance status

Patient‘s performance status assessed by clinicians on examination on diagnosis were recorded from medical folders as described by Gunn et al. (2013). This was classified as good (ECOG PS 0-2) and poor (ECOG PS 3-4).

3.2.8.2 Past medical history of patients Partient‘s history of mental health conditions and co-morbid conditions like hypertension, Ischemic Heart Disease (ISHD/ IHD), strokes or Transient Ischemic Attacks (TIAs), asthma, HIV/ AIDS, sickle- cell disease (SCD) and diabetes mellitus were recorded from medical folders.

3.2.8.3 Patients’ family history of cancer Patients‘ family history of cancer (especially parents, siblings and children) were recorded from medical folders.

3.2.8.4 History of presenting compliant of patients Patients‘ primary cancer sites, stages and classes of cancer and presence or absence of metastasis were recorded from medical folders.

i. Stages of cancer

Solid tumours presented by patients and staged by physicians as described by Edge (2010) were recorded from medical folders. This was classified as early (ie. stages 0, I and II) and advanced (ie. stages III and IV) cancers (Edge, 2010).

ii. Classes of cancer

The cancers presented by patients and classified as described by Modan et al. (2001) were recorded from medical folders.

3.2.8.5 Drug history of patients Chemotherapeutic agents and analgesics including doses, adjunctive anticancer treatments, herbal/ alternative therapies and date of initiation of anticancer treatments were recorded from patient‘s medical folders and the Hospital Administration Management Systems. Prescribed analgesics were assigned analgesic scores based on the WHO analgesic ladder (Dalton and Youngblood, 2000). If patients were prescribed analgesics from multiple levels of the WHO analgesic ladder, the assigned analgesic score was that of the most potent prescribed analgesic (Dalton and Youngblood, 2000). Analgesic scores of 0, 1, 2, and 3 were assigned for no analgesics, non-opioids, weak opioids and strong opioids respectively. Drug allergies and intolerances were also recorded. Additionally for female patients, history and current use of hormonal contraceptives and Hormone-Replacement Therapy were recorded from medical folders.

3.3 Data analyses Completed data collection forms were screened to ascertain the validity of the data and transferred unto computer spreadsheet (Microsoft Excel, 2013). Subsequently, Statistical Package for Social Scientists (SPSS) version 24.0 for windows® (IBM Corp, New York, USA) was used to analyse the data obtained.

3.4 Results

3.4.1 Socio-demographic characteristics of patients The mean age of patients was 53.54 years (SD = 15.46): males and females were 53.11 years (SD = 14.74) and 53.49 years (SD = 16.01) respectively. Females were 169 (82.8%) of the study sample (Figure 3.1). Majority of the patients were Christians; 178 (89.0%) and married; 104 (51.0%). The current occupations of patients (ISCO-08 classified) is shown in Table 3.1. Majority of patients resided in the Ashanti Region; 112 (57.5%) and were registered with the National Health Insurance Authority (NHIA) scheme; 167 (81.9%). A large number of patients; 146 (71.2%) were referred from other directorates within KATH to the Oncology Directorate while 23 (11.2%) were referred from other hospitals and clinics within the Kumasi metropolis eg. Peace and Love Hospital, Bomso Clinic, Tafo Hospital, Gary Marvin Hospital, Aninwaa Medical Centre and Afari Community Hospital to the Oncology Directorate, KATH. Other patients were also referred from other regions in Ghana: Brong Ahafo; 11 (5.4%), Central; 1 (0.5%), Greater Accra; 2 (1.0%), Northern; 3 (1.5%) and Western; 2 (1.0%). Majority of patients; 113 (79.6%) had Eastern Corperative Oncology Group classified performance status of 1. This is shown in Table 3.2. Nearly all patients 197 (97.0%) were alive when the study was completed. There was scanty data on patient‘s height, history of: alcohol consumption, tobacco use and recreational drug use (eg. cocaine, cannabis), predisposing diets and sexual habits/ preferences. For female patients, mean parity of 4.39 children (SD = 3.118) and mean age at menarche of 15.82 years (SD = 1.75) were recorded. There was however very little or no data on age at first live birth, last menstrual period, gravidity, abortus status, menopausal status and age at menopause.

Table 3.1: Frequency statistics for current occupations of patients involved in the study Occupation ISCO-08 major Frequency Percentage of group patients Managers 1 2 1.0 Professionals 2 19 9.6 Technicians and Associate 3 0 0 professionals Clerical support workers 4 1 0.5 Service and sales workers 5 73 36.9 Skilled Agricultural, Forestry 6 72 36.4 and Fishery workers Craft and related trades 7 9 4.5 workers Plant and machine operators 8 2 1.0 and assemblers Elementary occupations 9 3 1.5 Armed Forces occupations 0 17 8.6 Total 198 100.0 ISCO-08 = International Standard Classification of Occupations-2008

Figure 3.1: Age distribution per gender of patients involved in the study.

Table 3.2: Frequency statistics for performance status of patients (n = 204) ECOG PS Percentage of patients 1 79.6 2 13.4 3 5.6 4 1.4 ECOG PS = Eastern Coorperative Oncology Group performance status

3.4.2 Patients’ family history of cancer Few patients; 17 (9.1%) had positive family history of cancer.

3.4.3 History of presenting compliant of patients The most common primary cancer site was the breast; 66 (37.7%) (Figure 3.2). Metastasis had occurred in 66 (32.5%) of patients. Carcinoma occurred in 164 (80.4%) of patients while stage III cancers; 25 (58.1%) were the commonest [Figures 3.3 (A and B)].

Stomach Lymph nodes Prostate Colorectum Bone Oropharynx Gynaecological

PRIMARY CANCER SITE CANCER PRIMARY Breast 37.7

0 10 20 30 40 PERCENTAGE OF PATIENTS (%)

Figure 3.2: Primary cancer sites of patients

Figure 3.3: Classes of cancer (A), Stages of cancer (B)

3.4.4 Past medical history of patients Hypertension was the commonest comorbid condition in patients, 40 (19.6%) (Figure 3.4).

Figure 3.4: Comorbid conditions of patients

3.4.5 Drug history of patients The commonly prescribed chemotherapeutic agents for patients were doxorubicin; 47 (40.5%), and paclitaxel; 42 (36.2%) as shown in Table 3.3. In terms of chemotherapeutic agent classes, anthracyclines which are antitumour antibiotics ; 48 (46.6%) were the commonest followed by platinum compounds (which are classified under alkylating agents and related compounds; 38 (36.9%) (Table 3.4). For combination chemotherapy, double therapy; 53 (46.5%) was most commonly prescribed followed by triple therapy; 23 (20.2%) and quadruple therapy; 6 (5.3%). In terms of opioids, morphine; 56 (52.8%) was most commonly prescribed followed by pethidine; 19 (17.9%) (Table 3.5). For non-opioids, diclofenac; 54 (50.9) was most commonly prescribed followed by paracetamol; 43 (40.6%) (Table 3.5). For analgesic combinations, double therapy; 26 (28.9%) was most commonly prescribed followed by triple therapy; 10 (11.1%) and quadruple therapy; 8 (8.9%).

In terms of adjunctive treatments administered to patients, radiotherapy; 66 (82.5%) was the commonest followed by surgery (13.8%) and haemotransfusion (3.8%). Brachytherapy was solely administered to patients with prostate and cervical cancer; 17 (9.3%). There were no records in medical folders on patient‘s utilisation of alternative or complementary therapies, drug allergies and drug intolerances. For female patients,

there were no records on hormonal contraceptives and Hormone-Replacement Therapy use.

Table 3.3: Frequency statistics for patients’ use of chemotherapeutic agents Chemotherapeutic agent Frequency Percentage of patients Cyclophosphamide 29 25.0 Melphalan 1 0.9 Carboplatin 8 6.9 Cisplatin 33 28.4 Oxaliplatin 3 2.6 Dacarbazine 5 4.3 Methotrexate 3 2.6 5-flouracil 17 14.7 Capecitabine 1 3.0 Gemcitabine 1 0.9 Doxorubicin 47 40.5 Epirubicin 1 0.9 Bleomycin 3 2.6 Paclitaxel 42 36.2 Docetaxel 1 0.9 Vincristine 6 5.2 Etoposide 3 2.6 Goserelin 2 1.7 Tamoxifen 7 6.0 Anastrozole 3 2.6

Table 3.4: Frequency statistics for patients’ use of chemotherapeutic agent classes Class of chemotherapeutic agent Frequency Percentage of patients Nitrogen mustards 35 34.0 Platinum compounds 38 36.9 Folate antagonists 3 2.9 Pyrimidine analogues 30 29.1 Anthracyclines 48 46.6 Vinca alkaloids 6 5.8 Other plant derivatives 3 2.9 Hormone analogues 2 1.9 Hormone antagonists 7 6.8 Aromatase inhibitors 3 2.9 Total 175 169.9

Table 3.5: Frequency statistics for prescribed analgesics for patients’ (n=204) Analgesic Frequency Percentage of patients Diclofenac 54 50.9 Paracetamol 43 40.6 Morphine 56 52.8 Tramadol 13 12.3 Pethidine 19 17.9 Ibuprofen 4 3.8 Dihydrocodeine 13 12.3 Total 202 190.6

3.4.5.1 Relationships between medications and primary cancer sites of patients Doxorubicin and paclitaxel were predominantly used in the management of breast cancer as shown in Table 3.6. Anthracyclines, nitrogen mustards, and pyrimidine analogues were predominantly used in the management of breast cancer while platinum compounds were commonly used in the management of gynaecological cancers (Table 3.7). Morphine, diclofenac and paracetamol were commonly prescribed for patients with breast cancer while pethidine was commonly prescribed for patients with gynaecological cancers (Table 3.8).

Table 3.6: Relationship between chemotherapeutic agents and primary cancer sites of patients Chemotherapeutic Primary cancer site Frequency Percentage of agent patients Doxorubicin Breast 36 90.0 Colorectal 1 2.5 Prostate 1 2.5 Bone 2 5.0 Total 40 100.0 Paclitaxel Breast 23 57.5 Gynaecological 10 25.0 Colorectal 1 2.5 Bone 2 5.0 Stomach 2 5.0 Other 2 5.0 Total 40 100.0

Table 3.7: Relationship between classes of chemotherapeutic agents and primary cancer sites of patients Chemotherapeutic agent Percentage of Primary cancer site Frequency classes patients Anthracyclines Breast 36 87.9 Colorectal 4 2.4 Prostate 1 2.4 Bone 2 4.9 Stomach 1 2.4 Platinum compounds Gynaecological 16 48.5 Colorectal 4 12.1 Oropharyngeal 6 18.2 Stomach 3 9.1 Lymph node 1 3.0 Bone 1 3.0 Prostate 1 3.0 Other 1 3.1 Nitrogen mustards Breast 26 100 Pyrimidine analogues Breast 19 67.9 Colorectal 4 2.4 Lymph node 4 14.3 Stomach 1 3.6

Table 3.8: Relationship between commonly prescribed analgesics and primary cancer sites of patients Analgesic Primary cancer site Frequency Percentage of patients Morphine Breast 21 42.0 Gynaecological 13 26.0 Colorectal 2 4.0 Lymph node 1 2.0 Oropharyngeal 5 10.0 Prostate 3 6.0 Bone 4 8.0 Stomach 1 2.0 Pethidine Breast 1 5.3 Gynaecological 17 89.5 Colorectal 1 5.2 Diclofenac Breast 20 47.6 Gynaecological 14 33.3 Colorectal 2 4.8 Lymph node 1 3.5 Oropharyngeal 2 8.4 Stomach 2 1.0 Other 1 1.4 Paracetamol Breast 18 45.0 Gynaecological 13 24.0 Colorectal 1 2.5 Lymph node 1 2.5 Oropharyngeal 4 10.0 Prostate 1 2.5 Bone 1 2.5 Stomach 1 2.5

3.5 Discussion The average age of patients (M = 53.54, SD = 15.46) corroborates the observation by Ahrensberg et al. (2012) which implicates the aetiology of sporadic cancers to cell DNA damage over time. Cancers do not occur only in adults as it has been established that neuroblastoma commonly occur in children or adolescents (Ahrensberg et al., 2012).

Majority of patients being females; 169 (82.8%) is representative of the gender distribution of outpatients who visited the Oncology Directorate, KATH for anticancer treatments in 2015. Puri et al. in 2014 observed that females were more likely to seek health care at the hospital when sick than males. Reasons ascribed to this include the fact that males perceive themselves to be strong, stoic, masculine and self-sufficient (Puri et al., 2014). They usually remain silent on matters relating to their health and well-being and may even adopt risker lifestyle behaviours (Puri et al., 2014). It is not surprising that breast cancer; a female predominant cancer was the commonest in patients. Furthermore, treatments for female predominant cancers (eg. breast and gynaecological) are captured under the NHIA scheme in Ghana (Clegg-Lamptey et al., 2009). Hence, female predominant cancers are more likely to be reported promptly to the hospital for treatments than other cancers whose treatments are not captured under the NHIA scheme.

Majority of patients being married; 104 (51.0%) could help as these patients may do well in terms of treatments because they may have the advantage of their spouses‘ financial and emotional assistance and may be able to afford anticancer treatments and attend hospital regularly compared to single, divorced, separated and widowed patients (Roth et al., 2005). For widowed; 52 (25.5%), divorced; 14 (6.9%) and separated; 3 (1.5%) patients, losing a spouse or partner through death, divorce or separation could have been associated with emotional and psychological problems which could have influenced their cancer pain experience. These patients therefore needed psychological encouragement to help them cope better with the disease and comply with treatments. The associated loss of intimacy, loss of social connections and the financial constraints which may accompany being single, divorced, separated or widowed could have serious implications on the health and wellbeing of these patients.

Religion either Christianity and Islam can increase hope, optimism and reduce fear, depression, anxiety which can positively affect the cancer experience of religious

patients. Due to these psychosocial adjustments, religious patients; 200 (97.6%) were expected to cope better with anticancer treatments and have better quality of life than non-religious patients.

Jobless patients; 80 (39.2%) might not have a reliable/regular source of income which could have affected their ability to afford anticancer treatments especially if not covered under the NHIA scheme. Cancer and its treatments could have precipitated a lot of physical and psychological problems that might have adversely affected the ability of these patients‘ to work. Concurrent co-morbid conditions could have also adversely affected the employability of these patients. Service and sales workers; 73 (36.9%) who formed majority of employed patients had increased risk of developing malignant mesothelioma, leukaemia and lung cancer as they were likely to be exposed to UV rays from the sun, benzene, beryllium, cadmium, chromium, silica and environmental pollutants.

Patients who had NHIA registration; 167 (81.9%) did not have to pay for anticancer treatments directly out-of-pocket. Another added advantage for these patients is the fact that they could access some specialist cancer care and treatments free of charge which could have lead to good disease prognosis.

The fact that some patients were referred from other regions in Ghana to the Oncology Directorate, KATH emphasises the scanty nature of specialised cancer diagnostic and treatment centres in Ghana. Unfortunately, these scanty cancer treatment centres are concentrated in the main urban areas of Ghana (that is Accra, Kumasi and Tamale) to the detriment of the rural population; although majority of cancer patients may be rural. This posses the danger of perpetual overcrowding in these cancer treatment centres. Cancer patients in rural communities may seek treatment from traditional healing systems practitioners. This can cause a huge problem as these patients cannot be possibly enumerated by cancer registries; unless, they resort to western medicine at very late and incurable stages of the disease, as is often the case.

Patients with ECOG PS 1 (79.6%) and ECOG PS 2 (13.4%) had good prognosis and could tolerate radical anticancer treatments (eg surgery, radiotherapy, chemotherapy) depending on the location of the malignancy which could lead to complete cancer remission. Patients with ECOG PS 3 (5.6%) and ECOG PS 4 (1.4%) had poor prognosis and palliative interventions as recommended by the American Society of

Clinical Oncology (ASCO) (Amdal et al., 2011) could have helped these patients. However, palliative interventions should be initiated as rapidly as practicable as highlighted in the epidemiological study by Al-Kindi et al. (2014).

Another limitation of this study is that there was scanty information on patient‘s history of alcohol consumption, tobacco and, recreational drugs use, predisposing diets and sexual habits/ preference of patients. Therefore it was difficult to link the incidence of patients‘ cancers to these predisposing factors. Furthermore, although there was scanty data on the weights of patients in their medical folders, their heights were not recorded and hence their body mass index (BMI); which is a very important predisposing factor could not be calculated.

Particular attention should have been paid to patients who had positive family history of cancer; 17 (9.1%) as they could acquire hereditary cancer syndromes. These patients could acquire aggressive cancers which were likely to metastasize. They could also develop mixed types of cancer which could have affected cancer treatment options, increase toxicities and follow-up care. They could have also developed rare cancers whose management requires special protocols as documented by Brune et al. (2006). Family history of cancer being present in a relatively small number of patients in this study is different from the results of previous studies where most patients had positive family history of cancer (Pharoah et al., 1997; Brune et al., 2006; Couch et al., 2015).

An average parity of female patients of 4.39 children (SD = 3.12) implied that these patients were exposed to the development of breast, colon, and gynaecological cancers as was observed by Freitas et al. in 1998. A possible reason for this phenomenon could have been because pregnancy exposes females to high levels of placenta-derived factors like alpha-fetoprotein (AFP), estriol (E3) and pregnancy-associated plasma protein-A (PAPP-A) which are carcinogenic as observed by Högnäs et al. (2016). Therefore multiparous and multigravidous female patients were exposed to the development of female predominant cancers; an observation which has been affirmed by Freitas et al. (1998).

An average age at menarche of 15.82 years (SD = 1.75) implied that female patients in this study were exposed to high levels of oestrogens over a relatively long time which could have increased their risk of developing cancer syndromes like breast cancer.

Owing to the fact that anti-cancer treatments like chemotherapy, hormone therapy, surgery (eg. ovariectomy/ oophorectomy) and radiotherapy could cause early menopause in perimenopausal female patients and erectile dysfunction in male patients which could have affected their sex lives and sexuality, it is critical that the menopausal statuses of female patients are well documented in medical folders to serve as a reference point for the selection of appropriate anticancer treatments by healthcare providers as suggested by Freitas et al. (1998). Accurate estimation of the last menstrual period of female patients was also critical as it could have helped to predict possible pregnancy in female patients which could have informed the type of anticancer treatments as suggested by Freitas et al. (1998).

Patients who had comorbid conditions (27.6%) were likely to have increased functional impairments and disabilities which could have compounded the stress imposed by cancer. Patients who had hypertension as a comorbid condition; 40 (19.6%) could have suffered from acute cancer pain syndromes which is associated with generalized sympathetic hyperactivity as observed by Caraceni (2001). Effective cancer pain assessment and pain relief strategies were therefore critical as these could have reduced high blood pressure and resolved existing hypertension in these patients. Patients with comorbid HIV/ AIDS (1.5%) could have developed Kaposi‘s sarcoma, non-Hodgkin‘s lymphoma and invasive cervical cancer. Compliance to prescribed antiretrovirals by these patients could increase their cluster of differentiation 4 (CD4) counts and prevent the development of these cancers. Patients who had comorbid diabetes mellitus (5%) could have disabilities (eg. amputations) which could contribute to Disability- Adjusted Life Years (DALYs) and worsen their pain experience. Medication compliance by these patients was critical as this could have lowered their blood glucose levels and improved their health.

There was appropriate prescribing of doxorubicin; the commonly prescribed chemotherapeutic agent for patients in this study which is highly commendable. Chemotherapeutic agent cocktails (or combinations) like double and triple therapies which were commonly administered to patients is also commendable as these cocktails are known to prevent resistance, increase synergism; reduce side effects and increases survival as observed by Mayer and Janoff (2007).

Although morphine is indicated in the management of moderate to severe pain in cancer patients as recommended by Prommer (2015), comprehensive cancer pain assessments

should have been done for such patients according to the tenets of the WHO analgesic ladder. Factors such as cost, worrying side effects, limited availability and tight governmental policies may warrant the replacement of opioids like morphine with oxycodone or hydromorphone as documented by Prommer (2015).

Patients might suffer from chronic cancer pain syndromes which might require the consumption of high doses of non-opioid analgesics for long periods of time. Although, diclofenac, paracetamol and other non-opioids are indicated in the management of mild to moderate cancer pain, their usage should have been backed by comprehensive pain assessments and the tenets of the WHO analgesic approach as suggested by Prommer (2015). Non-opioids could cause numerous adverse effects including ―analgesic- associated nephropathy‖ and GIT distress with chronic use as affirmed by Jonsson et al. (2011). Perhaps, COX-2 selective inhibitors like celecoxib and entoricoxib could have been considered for patients with chronic pain to mitigate the GIT toxicity associated with the traditional non-opioid analgesics.

3.6 Conclusion Sociodemographic and clinical characteristics of oncology outpatients did not directly affect their incidence of cancer. Cancer pain management approaches at the Oncology Directorate, KATH did not conform to the WHO standards.

3.7 Recommendations 1) Effective cancer education programs highlighting prevention, early diagnosis and treatments should be carried out in Ghana; especially among the youth. 2) The medical history of cancer patients should be comprehensively taken to facilitate complete pain assessment. 3) The tenets of the WHO analgesic ladder approach should be followed by prescribers. 4) Patients should be adequately educated on possible side effects and drug interactions that may occur with their medications. 5) Healthcare providers should be educated on effective pain relief strategies to the benefit of cancer patients.

CHAPTER FOUR

4.0 CANCER PAIN ASSESSMENT IN PATIENTS

4.1 Introduction Cancer pain being multidimensional in nature involves nociceptive, neuropathic, psychogenic and idiopathic mechanisms at multiple body sites. The occurrence of pain as a result of cancer can contribute to Disability Adjusted Life Years (DALYs) which can influence the quality of life of sufferers (Batalha and Mota, 2013; Black et al., 2011). According to Margo McCaffery (1968), pain is whatever the experiencing person says it is. Because pain is a subjective experience, patients‘ self-report should not be taken lightly especially after ruling out patients with confirmed Munchausen syndrome or factitious disorder imposed by self (Jung and Reidenberg, 2007). Although self-report is regarded the gold standard of oncology pain assessment, it has a myriad of challenges including the patient‘s mood, side effects of medication (eg. sedation and lethargy) and the patient‘s cognitive status (Kim et al., 2012). Cancer pain assessment should involve an overall appraisal of the factors that may influence a patient‘s expression and experience of pain (Upadhyay et al., 2014; McCaffery et al., 2000; Kapstad et al., 2010). Comprehensive pain assessment is considered vital for effective pain relief (Conaghan et al., 2015). Comprehensive cancer pain assessment should include these attributes: pain history (ie. location, intensity, radiation, and quality), temporal pattern of pain, palliative and provocative factors of pain, timing and duration of pain, effects of pain on the quality of life of patients, past trials of pain therapy [eg. OTC medications (paracetamol/ herbal remedies)], and bio psychosocial factors and physiological indicators of pain. This study seeks to assess pain intensity levels of oncology outpatients at the Komfo Anokye Teaching Hospital.

4.2 Methodology The study setting, design, duration and ethical clearance were as described in chapter 3.

4.2.1 Study population The study sample was consenting cancer outpatients who met the study‘s eligibility criteria.

4.2.2 Sampling procedure and sample size determination As already established, the total number of outpatients who were newly diagnosed with different types of cancer in 2014 at the Oncology Directorate, KATH was seven hundred and fourty seven (747). Out of this number (747), a representative sample was calculated with the Yamane‘s formula for sample size determination as described by Singh and Masuku (2014). The Yamane‘s formula for sample size determination is given as:

Where n = sample size, N = total patient population size and e = sampling error.

Given that the total count of patients who reported to the Oncology Directorate, KATH in 2014 with different cancers was 747, the Yamane‘s formula for sample size determination becomes: n=747/1+747(0.05)2 n=260.5 ~ 261

The estimated number of patients available for sampling was therefore two hundred and sixty one (261). A total of two hundred and four (204) patients representing a response rate of 78% were however interviewed using a pain assessment questionnaire (the Brief Pain Inventory-Long Form). The disparity between the estimated number of patients and the actual number interviewed for this study was due to factors like lack of verbal informed consent by patients.

A non-probabilistic sampling procedure was adopted for this study. This is because of the infinite nature of the study population and the absence of a well-defined sampling frame for probabilistic sampling.

4.2.3 Data collection instrument and translation The English version of the Brief Pain Inventory-Long Form developed by Cleeland and Ryan (1994) (Appendix 8) was employed to elicit relevant information from patients. It was important that the English version of the Brief Pain Inventory-Long Form was

translated in Twi language to allow the inclusion of patients who could not read English language. To ensure that the meaning of the questions on the Brief Pain Inventory-Long Form are maintained, a forward and back translation using the process of translation and adaptation of instrument recommended by the WHO was used.

4.2.4 Pre-testing of the data collection instrument The study questionnaire was pre-tested on a convenient sample of 20 patients who were not included in the study population but met the study‘s eligibility criteria one month prior to the commencement of the interviews. This was done to evaluate the feasibility, easy readability and comprehensibility of the questionnaire.

4.2.5 Patients’ consent/ ethical considerations Verbal informed consent was obtained from all eligible patients before commencement of the structured interviews. Patients were told the aims and objectives of the study and assured anonymity. Taking part in this study was entirely voluntary. Patients were told that they had the option to discontinue the interviews whenever they wanted in accordance with the Declaration of Helsinki for human research (Ohaeri et al., 2007; Ahmedzai et al., 2015; Andreassen et al., 2012). Patients were given the option to take breaks during the interviews if they were fatigued or distressed and were assured that the information they provide will not be used against them or affect their healthcare in any way. Patients were asked to provide their telephone numbers so that the researcher can contact them for clarifications if need be. Patients‘ confidentiality was maintained throughout the study by using their folder identification numbers instead of names for identification. Also, the computer used for data entry and processing was pass-worded to protect the identities of patients.

4.2.6 Procedure for interviews A face-to-face interview approach was adopted in this study because the level of literacy of majority of patients was low. Also, the face-to-face interview approach had the advantage of minimising missing data. The questionnaire was administered in either English or Twi language depending on the educational backgrounds or language preferences of patients. Ghana is a multilingual country with each region speaking a different language. Twi, a local language of the

indigenous people of the Ashanti Region of Ghana is spoken and understood by majority of Ghanaians; especially people from the Akan ethnic group.

4.2.7 Inclusion criteria of the study The study‘s inclusion criteria included: i. Consenting cancer outpatients who were 18 years or older attending clinic at the Oncology Directorate, KATH during the study period. ii. Cancer outpatients with confirmed pathological diagnosis of primary or metastatic disease. iii. Oncology outpatients with or without disease related pain for at least one month. iv. Cancer outpatients who could comprehend English or Twi language.

4.2.8 Exclusion criteria of the study The study‘s exclusion criteria included: i. Cancer patients with documented or observable psychiatric or neurological disorders (eg. dementia or psychosis). ii. Cancer outpatients who refused to give consent.

4.2.9 Pre-data analysis procedures The educational level of patients rated as 0-16 on the BPI was categorized as:  0-2 = ―no formal education‖  3-5 = ―primary or basic education‖  6-8 = ―elementary/ Junior Secondary School (JSS)/ Junior High School (JHS) education‖  9-10 = ―Senior Secondary School (SSS) or Senior Technical School or Vocational School education‖  11-12 = ―ordinary or advanced level education‖  13-14 = ―diploma level education‖  15-16 = bachelors level education.

The recorded primary occupations of patients were classified according to the ISCO-08 classification method as described by Ganzeboom and Treiman (2010).

Patient‘s ratings of the 4 pain intensity items on the BPI were categorized as described by Okuyama et al. (2004) as: 0 = no pain, 1–5 = mild pain (1), 6–9 moderate pain (2) and 10 = severe pain (3). Patient‘s ratings of the 7 functional interference items on the BPI were categorized as described by Okuyama et al. (2004) as: 0 = ―does not interfere‖, 1-5= ―least interferes‖, 6-9 = ―averagely interferes‖ and 10= ―completely interferes‖. The pain descriptors; ―aching‖, ―throbbing‖, ―shooting‖, ―stabbing‖, ―gnawing‖, ―sharp‖ and ―tender‖ were used to denote nociceptive cancer pain while ―burning‖, ―exhausting‖, ―tiring‖, ―penetrating‖, ―nagging‖, ―numb‖, ―miserable‖ and ―unbearable‖ were used to denote neuropathic cancer pain as described by Holtan and Kongsgaard (2009). The Pain Management Index (PMI) score for every patient was calculated by deducting ―pain worst‖ score from analgesic score as described by Donovan et al. (2008).

4.3 Data analyses Completed data collection forms were screened to ascertain the validity of the data and transferred unto computer spreadsheet (Microsoft Excel, 2013). Subsequently, Statistical Package for Social Scientists (SPSS) version 24.0 for windows® (IBM Corp, New York, USA) was used to analyse the data obtained. Internal consistency reliability of the questionnaire was computed from the data obtained as described by Aisyaturridha et al. (2006) and Peterson (1994). Reliability coefficients [ie. Cronbach‘s alpha (α) values] ≥ 0.70 were deemed acceptable reliability as described by Nedjat et al. (2008) and Bolarinwa (2015).

The ―Corrected Item-Total Correlation‖ (CITC) coefficients and ―Cronbach‘s alpha if item deleted‖ values were computed from SPSS output as described by Tesfaye et al. (2016) and Bolarinwa (2015). Items with correlation coefficient (r) values < 0.33 were considered to have poor correlation and could be considered for removal from the questionnaire as described by Tesfaye et al. (2016). Also, items which substantially improved the reliability of the questionnaire could be considered for deletion as described by Bolarinwa (2015).

Based on the fact that non-normal distributions were obtained for some variables, non- parametric tests such as Kruskal-Wallis H tests, Mann-Whitney U tests and Spearman‘s rank-order correlation test were computed.

Based on the fact that normal distributions were obtained for some variables, parametric tests such as Pearson‘s correlation test, Independent-Samples t-tests and One-way ANOVA were computed. Post-hoc Tukey honestly significant difference (HSD) test was computed to determine the pairwise differences among group means. Multiple linear regression analysis was also computed to determine how a set of independent variables (predictors) predicted the overall quality of life (dependent variable) of patients.

The regression equation; y = b1x1+ b2x2 + b3x3 + … + c; was then derieved where y = estimated dependent variable, c = constant, b = regression coefficients and x = each independent variable.

Using a confidence interval (CI) of 95%, statistical significance was set at p ≤ 0.05, p ≤ 0.01 and p ≤ 0.001.

4.4 Results

4.4.1 Sociodemographic characteristics of patients The sociodemographic characteristics of patients are described in Table 4.1. Table 4.1: Sociodemographic characteristics of patients Frequency Percentage of patients Gender Females 169 82.8 Age (years) 40-49 49 24.0 Marital status Married 104 51.0 Educational status No formal education 83 40.7 Employment status Unemployed 80 39.2 Occupation of patients Sales and service 73 36.9 (ISCO-08) workers Occupation of patients‘ Sales and service 42 35.6 spouses (ISCO-08) workers ISCO-08 – International Standard Classification of Occupations – 2008

4.4.2 History of presenting compliant of patients Majority of patients; 136 (67.0%) indicated that pain was one of their symptoms when they first received their cancer diagnosis. Some patients; 81 (40.1%) had chronic cancer pain. Majority of patients; 31 (30.4%) reported abdominal pain (Table 4.2). Based on pain at its worst scores, majority of patients (56.9%) had severe cancer pain (Table 4.3). Pain at its worst had the highest mean rating for the pain intensity index subscale; (M = 2.461, SD = 0.685) while sleep (M = 2.28, SD = 0.736) had the highest mean rating for the functional interference index subscale (Table 4.4). Some patients (39.5%) cited that their pain palliative factors were rest, use of pain medications, sleep and a combination of rest and pain medications while 60.9% cited their pain aggravating factors as engaging in physical activities, walking, resting, lifting objects, hunger, a combination of hunger and physical activity, stress, standing and non- compliance to prescribed pain medications. The causes of patients‘cancer pain were the effects of anticancer treatments; 20 (18.9%), cancer itself; 79 (74.5%) and an unrelated non-malignant condition; 32 (30.2%). Nociceptive cancer pain; 123 was more popular in patients than neuropathic cancer pain; 85. The most popular nociceptive and neuropathic cancer pain descriptors were ―aching‖; 45 and ―tiring‖; 17 respectively (Table 4.5). Cancer pain commonly completely interfered with patients‘ sleep (46.2%) (Table 4.6).

Table 4.2: Frequency statistics for pain location of patients (n=153) Pain location Frequency Percentage of patients Head 16 10.5 Neck 4 2.6 Chest 6 3.9 Right breast 14 9.2 Left Breast 19 12.4 Abdomen 31 20.3 Cervix 11 7.2 Womb 6 3.9 Waist 7 4.6 Right hand 4 2.6 Left hand 8 5.2 Right thigh 9 5.9 Left thigh 7 4.6 Knee 2 1.3 Legs 2 1.3 Spine 1 0.7 Other areas 6 3.9 Total 153 100.0

Table 4.3: Reported severity of pain intensity items of patients (n=102) Pain categoriesPain at its worst (%) Pain at its least Pain at its average Pain now (%) (%) (%) None - 1.0 - 2.9 Mild 10.7 61.8 13.7 24.6 Moderate 32.4 33.3 77.5 52.9 Severe 56.9 3.9 8.8 19.6

Table 4.4: Pain intensity and functional interference indices items Item M SD Pain at its worst 2.461 0.685 Pain at its average 1.95 0.475 Pain now 1.89 0.743 Pain at its least 1.40 0.585 General activity 2.28 0.709 Mood 2.16 0.686 Walking ability 2.24 0.734 Normal work 2.07 0.664 Relationship with other people 2.09 0.662 Sleep 2.28 0.736 SD = Standard deviation

M = Mean

Table 4.5: Reported pain quality of patients Pain descriptor Frequency Aching 45 Throbbing 11 Shooting 24 Stabbing 4 Gnawing 7 Sharp 14 Tender 18 Burning 15 Exhausting 15 Tiring 17 Penetrating 16 Nagging 4 Numb 6 Miserable 6 Unbearable 6 Total 208

Table 4.6: Reported functional interference of patients (n=102) Extent of General Mood Walking Normal Relations Sleep Enjoyment interference activity ability work with of life other people No 0.9 1.0 0.9 1.0 1.0 interference Least 16.0 18.0 17.8 18.9 18.1 16.0 19.0 interference Average 40.6 50.5 41.8 54.7 54.2 37.7 60.0 interference Complete 42.5 30.5 40.4 25.6 13.7 46.2 20.0 interference Total 100 100 100 100 100 100 100

4.4.3 Past surgical history of patients About a quarter of patients; 51 (25.2%) reported undergoing surgery in the month prior to the interviews of which mastectomy; 19 (55.9%) was the commonest (Table 4.7).

Table 4.7: Frequency statistics for types of surgery undergone by patients (n = 34) Type of surgery Frequency Percentage of patients Mastectomy 19 55.9 Colostomy 2 5.9 Thyroidectomy 1 2.9 Prostatectomy 4 11.9 Ovariectomy 1 2.9 Hysterectomy 3 8.8 Fibriod 1 2.9 Cesarian section 1 2.9 Hernia repair 2 5.9 Total 34 100.0

4.4.4 Drug history of patients Strong opioids were commonly prescribed for patients; 64 (62.1%), followed by non- opioids (25.3%) and weak opioids (12.6%). About a third of patients (36%) reported taking pain medication(s) in the last 7 days. Patients preferred to take their pain medication ―on a regular basis‖; 56 (53.3%), ―only when necessary‖; 46 (43.8%) and ―do not take pain medicine‖; 3 (2.9%). About a half of patients; 42.5% reported that their pain relief duration after medication intake was more than 12 hours (Figure 4.5). Majority of patients; 55.2% reported that their pain medication intake in 24 hours was 1-2 times (Figure 4.1).

Patients felt they needed a stronger type of pain medication; 37 (34.9%) and needed to take more quantities of the pain medication than their doctors had prescribed; 22 (20.8%). Few patients; 9 (8.5%) were concerned that they used too much pain medication and cited fear of addiction as their primary concern. Few patients; 12 (11.4%) reported having problems with side effects from their pain medications and cited nausea and vomiting, bloating, constipation, general bodily pains and pruritus as the culprits.

Majority of patients; 57 (54.3%) felt they needed to receive further information about their pain medication. Relaxation techniques; 56 (60.2%) was one of the commonest adjunctive pain alleviation technique used by patients as shown in Figure 4.2.

Medications not prescribed by their doctors which patients took for managing their pain were OTCs like paracetamol and home remedies like herbal medicines.

Figure 4.1: Patients’ pain duration after taking pain medication.

Figure 4.2: Patients’ pain medication intake within a 24 hour period.

Figure 4.3: Complementary pain alleviation techniques used by patients

i Pain Management Index of patients Majority of patients; 61 (57.5%) were over-treated for cancer pain by the WHO standards (Table 4.8). Table 4.8: Pain Management Index of patients PMI Frequency Percentage of patients -2 10 9.4 -1 7 6.6 0 28 26.4 1 24 22.6 2 6 5.7 3 31 29.2 Total 106 100.0 PMI = Pain Management Index

Internal consistency (reliability)

The reliabilities of pain intensity index, functional interference index and whole scale are shown in Table 4.9. No pain intensity or functional interference item merited consideration for removal from their respective subscales as shown in Table 4.10. Pain at its average was the best item in the pain intensity index subscale (r = 0.702; α = 0.672) while normal work was the best item in the functional interference index subscale (r = 0.820; α = 0.883) as shown in Table 4.10.

Table 4.9: Reliabilities of the pain intensity index, functional interference index and whole scale Item Cronbach’s α coefficient No of items Pain intensity index 0.784 4 Functional interference 0.907 7 index Whole scale 0.876 11

Table 4.10: “Corrected Item-Total Correlation” coefficients and “Cronbach's alpha if item deleted” values for pain intensity and functional interference indices items Item “Corrected Item-Total “Cronbach's alpha if Correlation” coefficients item deleted” values Pain at its worst 0.537 0.761 Pain at its least 0.533 0.760 Pain at its average 0.702 0.672 Pain now 0.603 0.725 General activity 0.770 0.887 Mood 0.709 0.896 Walking ability 0.788 0.885 Normal work 0.820 0.883 Relationship with other 0.817 0.883 people Sleep 0.593 0.906 Enjoyment of life 0.577 0.907

Non-parametric tests

i. Spearman’s rank-order correlation tests

With the exception of ―pain at its least‖ and ―pain now‖ (ƿ = -0.03, p > 0.05), there was a significant positive association (p < 0.01) between all four pain intensity items (Figure 4.9). There was a significant positive association between all functional interference items ranging from ―mood‖ and ―normal work‖ (ƿ = 0.78, p < 0.05) to ―sleep‖ and ―general activity‖ (ƿ = 0.51, p < 0.05) (Table 4.11).

Figure 4.4: Spearman’s rank order correlation between the four pain intensity items. Correlation is significant at the 0.01 level (2-tailed)

Table 4.11: Spearman’s rank order correlation between functional interference items General Mood Walking Normal Relationship Sleep Enjoyment activity ability work with other of life people General 0.740* 0.707* 0.703* 0.706* 0.505* 0.383* activity Mood 0.641* 0.783* 0.707* 0.523* 0.411* Walking 0.669* 0.739* 0.621* 0.417* ability Normal work 0.733* 0.567* 0.493* Relationships 0.509* 0.547* Sleep 0.483* Enjoyment *Correlation is significant at the 0.05 level (2-tailed)

ii. Mann-Whitney U tests

Cancer patients who had not undergone surgery in the past month had higher pain intensity index scores; (M = 7.91, SD = 1.637) than those who had undergone surgery in the past month; (M = 7.21, SD = 1.799). Mann-Whitney U analysis revealed that there was a significant relationship between pain intensity index and the ranked means of the surgical history of patients in the past month (U = 851.000, p = 0.04) (Table 4.12). Patients who had not undergone surgery in the past month had higher functional interference index scores; (M = 15.91, SD = 3.640) than those who had undergone surgery in the past month; (M = 15.00, SD = 4.553). Mann-Whitney U analysis revealed that there was no significant association between functional interference index and the ranked means of the surgical history of patients in the past month (U = 1103.000, p = 0.794) (Table 4.13). Patients who had undergone brachytherapy had higher pain intensity index scores; (M = 8.22, SD = 0.833) than those who had not undergone brachytherapy; (M = 7.57, SD = 1.839). Mann-Whitney U analysis revealed that there was no significant relationship between pain intensity index and the ranked means of the brachytherapy history of patients (U = 289.500, p = 0.278) (Table 4.14). Patients who had undergone brachytherapy had higher functional interference index scores; (M = 16.00, SD = 4.330) than those who had not undergone brachytherapy; (M = 14.95, SD = 4.079). Mann-Whitney U analysis revealed that there was no significant association between functional interference index and the ranked means of the brachytherapy history of patients (U = 307.500, p = 0.409) (Table 4.15).

Table 4.12: Mann-Whitney U analysis of the surgical history of patients with pain intensity index Surgical history of M U p-value patients

No 7.91 851.000 0.04 Yes 7.21 M = mean Table 4.13: Mann-Whitney U analysis of the surgical history of patients with functional interference index Surgical history of M U p-value patients No 15.15 1103.000 0.794 Yes 15.00 M = mean

Table 4.14: Mann-Whitney U analysis of the brachytherapy history of patients with pain intensity index brachytherapy M U p-value history of patients No 7.57 289.500 0.278 Yes 8.22 M = mean

Table 4.15: Mann-Whitney U analysis of the brachytherapy history of patients with functional interference index brachytherapy M U p-value history of patients No 14.95 307.500 0.409 Yes 16.00 M = mean

iii. Kruskal-Wallis H tests

Patients who were 40-49 years had high pain severity index scores; (M = 8.30, SD = 1.915), followed by 70 years and above; (M = 8.23, SD = 1.481), 60-69 years; (M = 7.44, SD = 1.464), 50-59 years; (M = 7.36, SD = 1.497), 30-39 years; (M = 7.27, SD = 1.710) and patients under 30 years; (M = 6.25, SD = 2.062). Kruskal-Wallis H analysis revealed that there was no significant relationship between pain intensity index scores and the mean ranks of the age groups of patients; χ2(5) = 9.091, p = 0.105 (Table 4.16).

Patients who were 70 years and above; (M = 16.93, SD = 3.772) had high functional interference index scores followed by 30-39 years; (M = 16.60, SD = 3.680), 50-59 years; (M = 15.10, SD = 3.948), 60-69 years; (M = 14.65, SD = 3.101) and patients under 30 years; (M = 12.00, SD = 2.944). Kruskal-Wallis H analysis revealed that there was no significant association between functional interference index scores and the mean ranks of the age groups of patients; χ2(5) = 10.299, p = 0.067 (Table 4.17). Divorced patients had high pain severity index scores; (M = 9.13, SD = 1.553), followed by separated patients; (M = 8.50, SD = 0.707), widowed patients; (M = 7.88, SD = 1.764), married patients; (M = 7.53, SD = 1.784) and single patients; (M = 7.39, SD = 1.378). Kruskal-Wallis H analysis revealed that there was no significant relationship between pain intensity index scores and the mean ranks of the marital statuses of patients; χ2(4) = 8.670, p = 0.70 (Table 4.18). Divorced patients had high functional interference index scores; (M = 18.12, SD = 3.044), followed by widowed patients; (M = 16.07, SD = 3.195), separated patients; (M

= 16.00, SD = 1.414), married patients; (M = 14.68, SD = 4.212) and single patients; (M = 14.44, SD = 3.666). Kruskal-Wallis H analysis revealed that there was no significant association between functional interference index scores and the mean ranks of the marital statuses of patients; χ2(4) = 6.225, p = 0.183 (Table 4.19). Plant, machine operators and assemblers had high pain severity index scores; (M = 10.00, SD = 0.100), followed by elementary occupation workers; (M = 8.33, SD = 0.577), sales and service workers; (M = 8.33, SD = 0.577), skilled agricultural, forestry and fishery workers; (M = 7.70, SD = 2.093), other occupations; (M = 7.45, SD = 1.293), craft and related trades workers; (M = 7.20, SD = 0.837) and professionals; (M = 6.43, SD = 0.571). Kruskal-Wallis H analysis revealed that there was no significant relationship between pain intensity index scores and the mean ranks of the occupations of patients; χ2(6) = 8.561, p = 0.200 (Table 4.20). Plant, machine operators and assemblers had high functional interference index scores; (M = 19.00, SD = 0.100), followed by craft and related trades workers; (M = 16.20, SD = 3.194), elementary occupation workers; (M = 16.00, SD = 3.464), other occupations; (M = 15.91, SD = 4.527), skilled agricultural, forestry and fishery workers; (M = 15.20, SD = 4.444), sales and service workers; (M = 14.83, SD = 3.730) and professionals; (M = 12.29, SD = 2.430). Kruskal-Wallis H analysis showed that there was no significant association between functional interference index scores and the mean ranks of the occupations of patients.; χ2(6) = 7.175, p = 0.305 (Table 4.21). Oropharyngeal cancer patients had high pain intensity index scores; (M = 8.86, SD = 1.574), followed by gynecological cancer; (M = 8.33, SD = 1.047), breast cancer; (M = 7.60, SD = 1.841), bone cancer; (M = 7.57, SD = 1,718), lymphoma; (M = 7.50, SD = 0.707), ovarian cancer; (M = 7.33, SD = 1.1841), other cancers; (M = 7.00, SD = 2.646), prostate cancer; (M = 6.00, SD = 2.000) and stomach cancer; (M = 6.00, SD = 1.414). Kruskal-Wallis H analysis revealed that there was no significant association between pain intensity index scores; χ2(9) = 11.160, p = 0.265 and the mean ranks of the primary sites of cancer of patients (Table 4.22). Gynecological cancer patients had high functional interference index scores; (M = 17.71, SD = 2.867), followed by oropharyngeal cancer; (M = 16.63, SD = 3.452), patients with other cancers; (M = 16.33, SD = 6.429), stomach cancer; (M = 15.50, SD = 4.950), ovarian cancer; (M = 15.67, SD = 3.215), breast cancer; (M = 14.47, SD = 3.763), lymphoma; (M = 14.50, SD = 0.707), bone cancer; (M = 12.83, SD = 2.927), colorectal cancer; (M = 12.50, SD = 6.351), and prostate cancer; (M = 12.00, SD = 63

4.359). Kruskal-Wallis H analysis showed that there was no significant association between functional interference index scores and the mean ranks of the primary sites of cancer of patients.; χ2(9) = 13.588, p = 0.138 (Table 4.23).

Table 4.16: Kruskal-Wallis H analysis of age groups with pain intensity index Age group M χ2 df p-value under 30 6.25 9.091 5 0.105 30-39 7.27 40-49 8.30 50-59 7.36 60-69 7.44 70 and above 8.23 Total 7.71 df = degrees of freedom M = Mean

Table 4.17: Kruskal-Wallis H analysis of age groups with functional interference index Age group M χ2 df p-value under 30 12.00 10.299 5 0.067 30-39 16.60 40-49 14.32 50-59 15.10 60-69 14.65 70 and above 16.93 Total 15.14 df = degrees of freedom M = Mean

Table 4.18: Kruskal-Wallis H analysis of marital status with pain intensity index Marital status M χ2 df p-value Single 7.39 8.670 4 0.070 Married 7.53 Separated 8.50 Divorced 9.13 Widowed 7.88 df = degrees of freedom

M = Mean

Table 4.19: Kruskal-Wallis H analysis of marital status with functional interference index Marital status M χ2 df p-value Single 14.44 6.225 4 0.183 Married 14.68 Separated 16.00 Divorced 18.12 Widowed 16.07 df = degrees of freedom M = Mean

Table 4.20: Kruskal-Wallis H analysis of current occupation of patients with pain intensity index Current occupation of patients M χ2 df p-value Professionals 6.43 8.561 6 0.200 Service and sales workers 7.88 Skilled Agricultural, Forestry and Fishery 7.70 workers Craft and related trades workers 7.20 Plant and machine operators and assemblers 10.00 Elementary occupations 8.33 Armed Forces occupations 7.45 Total 7.66 df = degrees of freedom M = Mean

Table 4.21: Kruskal-Wallis H analysis of current occupation of patients with functional interference index Current occupation of patients M χ2 df p-value Professionals 12.29 7.175 6 0.305 Service and sales workers 14.83 Skilled Agricultural, Forestry and Fishery 15.20 workers Craft and related trades workers 16.20 Plant and machine operators and assemblers 19.00 Elementary occupations 16.00 Other occupations 15.91 Total 15.05 df = degrees of freedom M = Mean

Table 4.22: Kruskal-Wallis H analysis of primary site of cancer of patients with pain intensity index Primary site of cancer M χ2 df p-value Breast 7.60 7.175 6 0.305 Cervix, uterus & vulva 8.33 Ovary 7.33 Colorectal 7.00 Non-Hodgkin's lymphoma 7.50 Oropharyngeal 8.86 Prostate 6.00 Sarcoma 7.57 Stomach 6.00 Other 7.00 Total 7.67 df = degrees of freedom M = Mean

Table 4.23: Kruskal-Wallis H analysis of primary site of cancer of patients with functional interference index Primary site of cancer M χ2 df p-value Breast 14.47 7.175 6 0.305 Cervix, uterus & vulva 17.71 Ovary 15.67 Colorectal 12.50 Non-Hodgkin's lymphoma 14.50 Oropharyngeal 16.63 Prostate 12.00 Sarcoma 12.83 Stomach 15.50 Other 16.33 Total 15.02 df = degrees of freedom M = Mean

Parametric tests i. Pearson’s correlation analysis There was a statistically significant positive association between pain intensity index and functional interference index, r = .389. p < 0.01 (Figure 4.5). There was a significant negative correlation between PMI and pain intensity index after controlling for patients‘ functional interference index; (ƿ = -0.352, p < 0.01). There was no significant relationship between PMI and functional interference index after controlling for patients‘ pain intensity index (ƿ = -0.117, p > 0.01) (Figure 4.6).

Figure 4.5: Pearson correlation analysis between pain severity index and functional inteference index. Correlation is significant at p < 0.01 (2-tailed)

Figure 4.6: Scatter matrix between Pain Management Index, functional interference and pain intensity indices after controlling for a covariate. Correlation is significant at p < 0.01

ii. Independent Samples t-tests

From Table 4.24, females reported lower ―pain at its worst‖ scores; (M = 2.337, SD = 0.8624), ―pain at its least‖ scores; (M = 1.35, SD = 0.699), ―pain at its average‖ scores; (M = 1.88, SD = 0.832) and ―pain now‖ scores; (M = 1.79, SD = 0.828) when compared to males (M = 2.500, SD = 0.6070), (M = 1.45, SD = 0.605), (M = 2.10, SD = 0.447) and (M = 1.95, SD = 0.759) respectively. The results of a t-test analysis showed that there was no significant association between pain intensity index scores for both males and females (p > 0.05). From Figure 4.7, gynaecological cancer patients reported higher pain scores for pain at its worst, pain at its least, pain now and the overall pain severity index when compared to breast cancer patients. The scores of pain at its average for both gynaecological and breast cancer patients were almost the same. There was a significant association between pain now for both breast and gynaecological cancer patients (p < 0.05). From Figure 4.7, gynaecological cancer patients reported higher scores for general activity, mood, walking ability, normal work, relationship with other people, sleep, enjoyment of life and functional interference index. There was a significant association

between ―normal work‖, ―relationship with other people‖, ―sleep‖ and ―enjoyment of life‖ for both breast and gynaecological cancer patients (p < 0.05). Table 4.24: Independent Samples t-test analysis of pain intensity items and gender of patients Pain intensity Male (M) Female (M) T df p-value item Pain at its worst 2.500 2.337 0.80 104 0.43 Pain at its least 1.45 1.35 0.60 104 0.55 Pain at its average 2.10 1.88 1.12 104 0.26 Pain now 1.95 1.79 0.80 104 0.43 M = mean df = degrees of freedom

Figure 4.7: Scores for patients with breast and gynaecological cancers; a. functional interference items and b. pain intensity items. Bars represent M ± SD. * indicate p < 0.05

iii. One-way ANOVA Non-opioids were most prescribed for patients in terms of pain intensity index; (M = 8.56, SD = 1.944), followed by strong opioids; (M = 8.08, SD = 1.349) and weak opioids; (M = 6.67, SD = 2.309). One-way ANOVA showed no significant between- groups differences; F(2, 33) = 1.601, p = 0.217 (Table 4.25).

Strong opioids (M = 15.83, SD = 3.614) were most prescribed for patients in terms of functional interference index followed by weak opioids; (M = 15.67, SD = 1.528) and non-opioids; (M = 14.67, SD = 3.162). One-way ANOVA showed no significant between-groups differences; F(2, 32) = 0.377, p = 0.689 (Table 4.25).

Strong opioids; (M = 2.667, SD = 0.4815) were most prescribed for patients in terms of pain at its worst followed by non-opioids; (M = 2.556, SD = 0.7265) and weak opioids; (M = 1.667, SD = 0.5774). One-way ANOVA showed significant between-groups differences; F(2, 33) = 4.304, p = 0.022 (Table 4.26). Post-hoc comparisons using the Tukey‘s HSD test revealed that significantly differences occurred between patients who were prescribed weak opioids and strong opioids (p < 0.05) (Appendix 10). There were no significant differences between weak opioids and non-opioids (p > 0.05).

Table 4.25: Types of analgesics and pain intensity and functional interference indices of patients Item Types of analgesics M SD Df F p-value Non-opioids 8.56 1.944 Pain intensity Weak opioids 6.67 2.309 2 1.60 0.217 index Strong opioids 8.08 1.349 33 Functional Non-opioids 14.67 3.162 2 0.377 0.689 interference index Weak opioids 15.67 1.528 32 Strong opioids 15.83 3.614 df= degrees of freedom M = mean SD = standard deviation

Table 4.26: Types of analgesics and pain intensity items Pain Types of M SD df F p-value intensity analgesics items Pain at its Non-opioids 2.556 0.7265 2 4.304 0.022 worst Weak opioids 1.667 0.5774 33 Strong opioids 2.667 0.4815 Pain at its Non-opioids 1.78 0.667 2 0.436 0.650 least Weak opioids 1.67 0.577 33 Strong opioids 1.54 0.658 Pain at its Non-opioids 2.22 0.441 2 0.888 0.421 average Weak opioids 2.00 0.000 33 Strong opioids 1.96 0.550 Pain now Non-opioids 2.00 0.707 2 0.941 0.400 Weak opioids 1.33 1.155 33 Strong opioids 1.92 0.717 df= degrees of freedom M = mean SD = standard deviation

iv. Multiple linear regression analysis The model reached significance; it successfully predicted functional interference index of patients [F(1, 100) = 17.819, p < 0.001]. The model explained 15% of variance in functional interference index of patients (Table 4.27). Patients‘ functional interference index was predicted by their pain intensity index (β = 0.389, t = 4.221, p < 0.001) (Table 4.28). For an increase in pain intensity index by 1, functional interference index increased by 39.

Table 4.27: Results of ANOVA for functional interference index (dependent variable) and constant and pain severity index (predictors) Model df F Sig. 1 Regression 1 17.819 0.000b Residual 100 Total 101 R2 = 0.151 df= degrees of freedom` R2 = multiple correlation coefficient of determination b= predictors [(constant), pain severity index]

Table 4.28: Regression coefficientsa Modela B Β t Sig. 1 (Constant) 9.747 7.439 0.000 Pain severity index 0.698 0.389 4.221 0.000 a = dependent variable: Function interference index β = Beta coefficient Sig = p-value B = multiple regression coefficient

Multiple linear regression equation: Functional interference index of patients = 9.747 + 0.698 (pain severity index)

Relationship between variable Male patients were commonly prescribed non opioids; (31.3%) and weak opioids; (18.8%) when compared to females; (24.1%) and (11.5%) respectively. Female patients were commonly prescribed strong opioids; (64.4%) when compared to males; (50.0%) (Table 4.29). Female patients were likely to have their cancer pain optimally managed; (28.1%) when compared to males (17.6%) (Table 4.30). Morphine, pethidine and diclofenac were commonly prescribed for patients with severe pain while paracetamol was commonly prescribed for patients with moderate pain (Table 4.31). Patients with abdominal pain were likely to suffer severe pain (Table 4.32).

Table 4.29: Relationship between prescribed analgesics and gender of patients Gender Class of analgesic Frequency Percentage of patients Male Non opioids 5 31.3 Female 21 24.1 Male Weak opioids 3 18.8 Female 10 11.5 Male Strong opioids 8 50.0 Female 56 64.4 Total 103 100.0

Table 4.30: Relationship between gender and Pain Management Index of patients Gender PMI Frequency Percentage of patients Male -2 3 17.6 Female 7 7.9 Male -1 1 5.9 Female 6 6.7 Male 0 3 17.6 Female 25 28.1 Male 1 5 29.4 Female 19 21.3 Male 2 2 11.8 Female 4 4.5 Male 3 3 17.6 Female 28 31.5 Total 106 100.0 PMI = Pain Management Index

Table 4.31: Relationship between commonly prescribed analgesics and reported pain intensity of patients Analgesic Pain intensity Frequency Percentage of patients Morphine Severe 19 63.3 Moderate 11 36.7 Mild 0 0 Pethidine Severe 6 60.0 Moderate 4 40.0 Mild 0 0 Diclofenac Severe 19 65.5 Moderate 8 27.6 Mild 2 6.9 Paracetamol Severe 11 44.4 Moderate 13 52.0 Mild 1 4.0 Total 57 100.0

Table 4.32: Relationship between reported common pain location and pain intensity of patients Pain location Pain intensity Frequency Percentage of patients Abdomen Severe 21 70.0 Moderate 6 20.0 Mild 3 10.0 Left breast Severe 8 47.1 Moderate 7 41.2 Mild 2 11.8 Head Severe 8 50.0 Moderate 6 37.5 Mild 2 12.5 Right breast Severe 5 35.7 Moderate 7 50.0 Mild 2 14.3

4.5 Discussion The fact that majority of patients; 136 (66.0%) had pain at the time of cancer diagnosis has been corroborated by previous researchers (Al Qadire et al., 2013; Anton et al., 2012). Although there is direct correlation between pain severity and stage or location of cancer as established by Hoskins et al. (2010), there are notable painful cancer syndromes such as bone cancer and head and neck cancer (Hoskins et al., 2010, Argiris et al., 2008). Patients who had these cancers were therefore likely to report pain at the time of diagnosis.

The fact that majority (74.5%) of patients reported that their pain was caused by cancer has been noted by Caraceni and Portenoy (1999). Although the administration of antineoplastic therapies (eg. chemotherapy and radiotherapy) is known to be accompanied by painful syndromes such as 5-flourouracil- induced angina pain (Peters et al., 2000), this was not the leading cause of pain in patients.

According to Ballout et al. (2011), physical examination of cancer patients is usually focused on patients‘ reported site of pain but this should be guided by the presence of referred pain. For instance, hepatic metastases commonly refer pain to the shoulder while breast metastases commonly refer pain to the spinal cord (Ballout et al., 2011). Generally, the common sites of cancer metastasis are bone, brain, liver and lung (Chapman, 2012). The finding that cancer pain occurred most commonly in the abdomen of patients in this study; a finding affirmed by Ballout et al. (2011) should be guarded by the fact that the abdomen (although rarely) can be a site for referred cancer pain. Common sites of cancer pain reported by patients in other similar previous studies were head and neck (Batalha and Mota, 2013; Gunn et al., 2013), knee (Eggermont et al., 2009), lung (Caraceni and Portenoy, 1999) and low back (Roth et al., 2005). The spine as a site of cancer pain in patients in this study; 1 (1%) is most likely as a result of spinal cord compression due to metastatic breast cancer. The presence of multiple cancer pain sites reported by some patients in the current study has been corroborated by other researchers (Ballout et al., 2011; Bortsov et al., 2014; Donovan et al., 2008; Eggermont et al., 2009).

Patients who underwent various surgeries in the past month (eg. mastectomy, thoracotomy etc.) were likely to suffer from phantom limb pain and chronic post- surgical pain; both of which are chronic and neuropathic in nature as documented by Donovan et al. in 2008. These patients should often have their prescriptions reviewed to

manage their chronic pain as observed by Tessaro et al. (2010) and effectively manage their neuropathic pain using anticonvulsants like gabapentin or pregabalin as documented by Prommer (2015).

Nociceptive pain being commonly reported by patients in this study conforms to literature (Wilkie et al., 2010). The number of patients (123) who reported nociceptive pain was higher than that reported by patients in another study (Caraceni, 2001). These patients were likely to respond well to opioids and adjuvants like capsaicin. But caution should be taken in generalizing pain treatments for these patients as patients with visceral nociceptive pain may suffer from referred pain. Therefore, these patients should be thoroughly examined to ascertain the exact type of nociceptive pain they have.

Patients who suffered from neuropathic pain syndromes (85) were likely to experience referred pain, allodynia, hyperpathia or dysesthesia. They therefore required thorough physical examination by physicians. Review of their prescriptions to include adjuvant analgesics such as anticonvulsants (eg. Gabapentin and pregabalin) and antidepressants (eg. amitriptyline) to manage psychogenic pain associated with neuropathic pain is imperative.

Accurate assessment of pain intensity should be guided by the fact that patients who had neuropathic pain syndromes (85) were likely to experience allodynia and hyperpathia which can affect their pain severity ratings. Also patients who had acute cancer pain; 121 (59.9%) were likely to show overt pain behaviours like grimacing, moaning, and splinting which can also influence their pain severity ratings. The fact that ―pain at its worst‖ had the highest mean pain intensity rating (M = 2.46, SD = 0.685) has been affirmed by Ballout et al. (2011).

According to Guy-Coichard et al. (2008), cancer pain can cause significant psychological and physical impairments which can contribute to substantial interference with sufferers‘ functional abilities such as walking ability, mood and ability to work. This observation was affirmed in this study as cancer pain significantly interfered with patients‘ functional abilities. This same observation has been made by previous researchers (Guy-Coichard et al., 2008; Hadi et al., 2008).

With comprehensive cancer pain assessments and effective pain relief interventions for these patients, ancillary outcomes will include improved functional abilities which could enhance their quality of life. It appears that sleep of patients was most affected by

cancer pain as evidenced by high mean interference ratings (M = 2.28, SD = 0.736); an observation corroborated by Ballout et al. in 2011.

The fact that patients in this study reported that analgesics were not prescribed for all of them contradicts the findings by Ballout et al. (2011) in a study where all patients were prescribed analgesics. This finding should however be compared with prescribed analgesics for patients stated in their medical folders as they may not be able to recollect all prescribed medications. Opioids, being most commonly prescribed for patients in this study per their own self-report has been corroborated by Ballout et al. (2011). One clear defect in analgesic prescribing for patients in this study was the fact that prescribing was not done according to the WHO standard as recommended by Prommer (2015).

Patients who reported having pain that required medication every day; 51 (40.1%) had chronic or background cancer pain. According to Jensen et al. (2011), chronic cancer pain syndromes are known to be mostly due to the direct effects of malignancy. Hence it is important that the primary cancer(s) of these patients‘ are managed very well. As an immediate relief of these patients‘ pain, Around-the-Clock normal-release opioids such as immediate-release (IR) morphine, oxycodone or hydromorphone should have been administered 4 hourly or sustained-release (SR) morphine, oxycodone or hydromorphone 12 hourly as commended by Tessaro et al. (2010).

Furthermore, the use of tolerable, non-invasive complementary pain treatment modalities should have been encouraged in all patients especially those with chronic pain syndromes. This could have helped with efficient pain relief and reduced the side effect burden associated with long term use of analgesics as affirmed by Vargas- Schaffer (2010). Relaxation techniques being commonly used by patients; (60%) could help in the effective relief of their pain as postulated by Buyukyilmaz and Asti (2013). In other studies however, physiotherapy (Grosen et al., 2013) and massage therapy (Guy-Coichard et al., 2008) were commonly used by patients.

Patients who could predict their pain provoking factors had intermittent cancer pain and those who cited physical activity, walking, resting, lifting objects and standing as their pain exacerbating factors had incident cancer pain. Patients who cited stress and hunger as their pain exacerbating factors had spontaneous or breakthrough cancer pain. Early management and monitoring of these patients with breakthrough cancer pain could have

given an indication of disease progression or possibility of recurrence as well as prevention of CNS remodelling. Breakthrough cancer pain is also known to be associated with potential psychosocial and economic effects (Rustoen et al., 2013) which could have worsened the cancer pain experience of these patients. Patients who reported intermittent cancer pain required regular review of their prescriptions to include rescue doses of rapid-acting strong opioids like fentanyl (as buccal, lozenge, intranasal, film and sublingual dosage forms), adjuvant analgesics like gabapentin for the associated neuropathic pain and non-opioids. Rescue medications were warranted when patients‘ pain arose in advance of predictable pain triggers.

Patients who reported not taking their pain medicine every day; (29.5%), not taking pain medication at all; (2.9%), and taking medications only when necessary; (43.8%) acted contrary to the WHO analgesic ladder guidelines which stipulate Around-the- Clock dosing of prescribed analgesics. If these patients had acute pain, it was likely to develop into chronic pain because of poor pain control. Review of their prescriptions to include extended-release (ER, XL, XT, XR) opioids like SR hydromorphone which could have been administered every 12 hours or 24 hours or transdermal fentanyl/ buprenorphine formulations administered every 72 hours could have been helpful to these patients.

Patients who reported taking their pain medications on regular basis; (53.3%) stuck to the WHO analgesic ladder guidelines which stipulate Around-the-Clock dosing of prescribed analgesics as recommended by the WHO (2015). These patients were likely to experience continuous pain relief which would impede the development of chronic cancer pain and its attendant psychosocial sequelae.

Per the WHO analgesic ladder guidelines, analgesic prescribing and dosing should be individualized and dependent on pain patterns (Vargas-Schaffer, 2010). After comprehensive review of the prescriptions of patients who reported taking pain medications ―only when necessary‖; 46 (43.8%) by health care providers, opioids such as SR opioids, ER, IR and transdermal opioids (fentanyl/ buprenorphine) could progressively replace prescribed pain medications for these patients. Hopefully, this approach could foster compliance in these patients. The possibility of opioid-related side effects such as nausea and vomiting are prominent with the administration of these extended-release opioids especially in opioid naïve patients. Such patients should have therefore been identified and cautioned. Additionally, counselling by healthcare

providers on the effects of medication non-compliance might have been helpful to these patients. Also, these patients could have experienced end-of-dose failure pain and could have benefited from increasing doses or frequencies of prescribed regular analgesics.

Patients who felt they needed a stronger type of pain medication; (34.9%), those who felt they needed to take more quantities of their pain medication than the prescribed quantities by the doctor; (20.8%) and those in whom pain medications did not help at all; (1.9%) probably needed additional approaches to their pain control apart from prescribed systemic analgesic therapy. Radiotherapy and pain interventional therapies like nerve blocks, intrathecal drug therapy, kyphoplasty/ vertebroplasty, image-guided tumour ablation and complementary therapies could have helped in these instances. However, very importantly the possibility of counterfeit or substandard pain medications should also not be ruled out as it could have contributed to the unresolved pain of these patients.

Few patients; 12 (11.4%) experiencing side effects from prescribed analgesics in the current study contradict the findings by Beauregard et al. (1998) where 49% of patients experienced analgesic-related side effects. The commonest side effect reported by patients; nausea and vomiting could have been treated with antimetics (eg. metoclopramide) or neuroleptics (eg. haloperidol or olanzapine). Also, opioid rotation/ switch and a change in route of administration of pain medications could have been considered for these patients. Furthermore, the use of extended release opioid formulations could have reduced the opioid-related side effects reported by these patients as corroborated by Wallace et al. (2008) and Grosset et al. (2005).

Cronbach‘s α value of 0.78 obtained for the pain intensity index subscale in this study agrees with Cronbach‘s α values of 0.78 to 0.96 reported by other researchers in previous similar studies (Black et al., 2011, Ballout et al., 2011; Atkinson et al., 2010). The fact that deletion of any pain intensity item did not significantly affect the Cronbach‘s α value of the pain intensity index subscale has been corroborated by Ballout et al. (2011). Cronbach‘s α value of 0.907 obtained for the functional interference index subscale in this study lies within the range of Cronbach‘s α values (α = 0.83 to 0.95) computed by other researchers in similar previous studies (Ballout et al., 2011; Black et al., 2011). Deletion of functional interference items not significantly affecting the Cronbach‘s α coefficient of the functional interference index subscale has been corroborated by Ballout et al. (2011). The moderate correlation between all pain

intensity items (ƿ = ± 0 30 to ± 0 49) obtained in this study contradicts the results of the study by Ballout et al. (2011) where the correlation between pain intensity items was moderate to strong. The fact that there was strong correlation (ƿ ≥ ± 0.50) between all functional interference items varies from the results of the Ballout et al. (2011) study. The statistically significant correlation [r = 0.39, p = 0.001] between pain intensity index and functional interference index reported in this study is has been affirmed by Ballout et al. (2011).

Generally, patients were over-treated; 61 (57.5%) or under-treated; 17 (16%) for pain according to the WHO standards. Few patients; (26.4%) having optimum pain relief (PMI = 0) contradicts the results of the Donovan et al. study in 2008 where 78.6% of patients had optimum pain relief. Adequate assessment of these patients‘ pain is critical to optimal pain relief as established by Donovan et al. in 2008. Perhaps, the National Cancer Institute (NCI)- USA‘s recommendation of a Patient Reported Outcome Measurement System (PROMIS) to measure cancer-related subjective outcomes and the implementation of computer based pain assessment tools as documented by Cook et al. in 2015 could have been beneficial in achieving optimal pain relief to patients who had suboptimal pain relief.

4.6 Conclusion

Majority of patients had severe cancer pain.

4.7 Recommendations

1. Cancer patients with breakthrough pain should be advised to take rescue medications when pain arises or in advance of predictable pain triggers.

2. Cancer patients who experience chronic pain should be effectively managed using pharmacological and non-pharmacological means.

CHAPTER FIVE 5.0 HEALTH-RELATED QUALITY OF LIFE OF PATIENTS

5.1 Introduction Cancer can leave residual disabilities and functional loss, cause psychosocial consequences as well as depletion of the finances of patients and their carers (Alifrangis et al., 2011). It is therefore essential that the quality of life of cancer patients are assessed accurately as this can lead to interventions which can improve their Quality Adjusted Life Years (QALYs).

Cancer treatments could also change the appearance of sufferers by causing weight loss or gain, loss of a body part(s), surgical scarifications or having an ostomy. This can leave residual disabilities, contribute to Disability Adjusted Life Years (DALYs) and affect the quality of life of patients. Cancer treatments can also affect the sexuality and sexual function of sufferers and cause cancer-related psychological issues ranging from anxiety and depression to post-traumatic stress disorder (PTSD) and post-traumatic stress symptoms (PTSS). This can aggravate the cancer pain experience and affect the quality of life of sufferers (Paredes et al., 2010).

Due to the need for repeated visits to the hospital for anticancer treatments, cancer patients may suffer from social problems like inability to work or fulfil other normative social responsibilities, expensive treatments which can plunge them and their families into poverty. Indeed, the physical, psychological and social stressors that accompany cancer and its treatments are intertwined and contribute to each other.

Therefore, this study seeks to establish the influence of cancer pain on the quality of life of patients at the Komfo Anokye Teaching Hospital.

5.2 Methodology

The study design and site, study population, sample size determination and sampling procedure, pretesting of the study instrument, procedure for conduction of structured interviews, ethical clearance, patient‘s consent/ ethical considerations, inclusion and exclusion criteria of the study and data analysis were same as described in chapter 4.

5.2.1 Data collection instrument The English version of the World Health Organization Quality of Life Brief version developed by the World Health Organization (2004) (Appendix 9) was used to elicit relevant information from patients. It was important that the English version of the World Health Organization Quality of Life Brief version was translated in Twi language to allow the inclusion of patients who could not read English language. To ensure that the meaning of the questions on the World Health Organization Quality of Life Brief version are maintained, a forward and back translation using the process of translation and adaptation of instrument recommended by the WHO was used.

5.2.2 Pre-data analysis procedures Physical health domain (DOM 1) score was computed as the mean of questions 3, 4, 10, 15, 16 and 17; psychological health domain (DOM 2) score was computed as the average of questions 18, 5, 6, 7, 11, 19 and 26; social relationship domain (DOM 3) score was computed as the mean of questions 20, 21 and 22 and environmental health domain (DOM 4) score was computed as the average of questions 8, 9, 12, 13, 14, 23, 24 and 25.

Three negatively framed questions (3, 4 and 26) were reversed before computing domain scores as described by Tesfaye et al. (2016). Domain scoring was not done when ≥ 20% of items were missing from the answered instrument as described by Tesfaye et al. (2016). Domain scores were deemed unacceptable where ≥ 2 items were missing from one domain or where one item in the social relationships domain was missing as described by Tesfaye et al. (2016).

5.3 Results 5.3.1 Descriptive analyses

Socio-demographic characteristics of patients The average age of patients was 53.54 years (SD = 15.46). Majority of patients were females; 169 (82.8%), married; 104 (51.0%) and had no formal education; 83 (40.7%).

Health and quality of life ratings of patients Majority of patients rated their quality of life as good (40.7%) and were neither satisfied nor dissatisfied (41.7%) with their health as shown in Figures 5.1 A and B respectively. 80

Figure 5.1: A Quality of life ratings by patients and B. health ratings by patients.

Domains i Physical health domain Physical pain prevented almost all patients (95.2%) from doing what they wanted to do [Figure 5.2 (A)]. Nearly all patients (99.0%) needed medical treatment to function in their daily lives [Figure 5.2 (B)]. Most patients (61.3%) did not have enough energy for their everyday lives [Figure 5.2 (C)]. Majority of patients (79.9%) did not get around well [Figure 5.2 (D)].

Majority of patients were dissatisfied (55%) with their sleep [Figure 5.3 (A)]. Majority of patients were dissatisfied (66.9%) with their capacity to perform daily living activities [Figure 5.3 (B)]. Majority of patients (69.7%) were dissatisfied with their capacity for work [Figure 5.3 (C)].

Figure 5.2: Summary of physical health domain: (A) degree of restriction of patients’ activities by pain (B) patients’ need for medical treatment to function in their dialy lives (C) patients’ energy levels to function in their dialy lives and (D) extent of patients’ movement around

Figure 5.3: Summary of physical health domain: (A) extent of patients’ sleep disturbance (B) patients’ capacity for work and (C) patients’ ability to perform activities

ii Psychological health domain Majority of patients (70.5%) did not enjoy life [Figure 5.4 (A)]. Majority of patients (54.9%) felt their life was meaningful [Figure 5.4 (B)]. Majority of patients (60,8%) could not concentrate [Figure 5.4 (C)]. Majority of patients (75.5%) did not accept their bodily appearance [Figure 5.4 (D)]. Majority of patients (54.7%) were dissatisfied with themselves [Figure 5.5 (A)]. Majority of patients (70.1%) did not experience negative feelings such as blue mood, despair, anxiety and depression [Figure 5.5 (B)].

Figure 5.4: Summary of psychological health domain: (A) extent of patients’ enjoyment of life (B) extent of patients’ meaningfulness of life (C) extent of patients’ concentration and (D) extent of patients’ acceptance of body appearance

Figure 5.5: Summary of psychological health domain: (A) patients’ personal satisfaction and (B) patients’ negative feeling

iii Social relationships domain Majority of patients (72.4%) were dissatisfied with their personal relationships [Figure 5.6 (A)]. Majority of patients (71.4%) were dissatisfied with their sex lives [Figure 5.6 (B)]. Majority of patients (71.4%) were dissatisfied with the support they got from friends [Figure 5.6 (C)].

Figure 5.6: Summary of social relationship domain: (A) patients’ personal relationships (B) patients’ rating of sex life and (C) patients’ rating of social support

iv Environmental health domain Majority of patients (60.3%) did not feel safe in their daily lives [Figure 5.7 (A)]. Majority of patients (54.4%) felt their physical environment was not healthy [Figure 5.7 (B)]. Majority of patients (61.1%) did not have enough money to meet their needs [Figure 5.7 (C)]. Majority of patients (71.1%) did not have access to the information they needed in their day-to-day lives [Figure 5.7 (D)]. Majority of patients(72.5%) did not have the opportunity for leisure activities [Figure 5.8 (A)]. Majority of patients (52.7%) were dissatisfied with the conditions of their living places [Figure 5.8 (B)]. Majority of patients (62.6%) were dissatisfied with their ability to access health services [Figure 5.8 (C)]. Majority of patients (59.6%) were dissatisfied with their mode of transportation [Figure 5.8 (D)].

Figure 5.7: Summary of environmental health domain: (A) patients’ feeling of safety in daily lives, (B) patients’ feeling of healthy physical environment, (C) patients’ financial capacity and (D) patients’ access to information

Figure 5.8: Summary of environmental health domain: (A) patient’s opportunity for leisure (B) patients’ satisfaction with home (C) patients’ access to health services and (D) patients’ mode of transportation

Measures of central tendency and spread of individual items/ domains The question relating to patients‘ experience of negative feelings had the highest satisfaction rating (M = 3.872, SD = 1.18423) while the question relating to the sex life of patients had the lowest satisfaction rating (M = 2.563, SD = 1.1536) (Appendix 11). On the average, patients had moderate overall quality of life (M = 3.1647, SD = 0.11075). The psychological health domain had the highest satisfaction rating (M = 66.7615, SD = 11.91879) as shown in Table 5.1. Table 5.1: Domains of the WHOQoL-Bref Domain (Transformed) M SD Physical health 62.2658 10.93787 Psychological health 66.7615 11.91879 Social relationships 57.4797 14.13597 Environmental health 63.0556 10.04476 M = mean SD = standard deviation

Additional information on the questionnaire Majority of patients (73.5%) admitted that they were currently ill and that the interviewer or a health care practitioner helped them to fill out the questionnaire (98.0%). On the average, the interview completion time was 15 minutes (SD = 6.32).

5.3.2 Internal consistency (reliability) The Cronbach‘s α coefficient for the whole scale was 0.910. Three questions: 3 (r = 0.132, α = 0.914), 4 (r = 0.082, α = 0.914) and 22 (r =0.315, α = 0.911) could be considered for removal from the scale (Appendix 11). Question 19 was the best item on the scale (r = 0.748) (Appendix 11).

5.3.3 Non-parametric tests There was strong association between the domains of the WHOQoL-Bref (ƿ ≥ 50%). The strongest inter-domain correlation was between the physical health domain and psychological health domain (ƿ = 0.74) while the weakest was between the social relationships domain and physical health domain (ƿ = 0.54) (Table 5.2). There was significant association between: physical health domain and psychological health domain (p < 0.01) and between physical health domain and social relationships domain (p < 0.001) (Figure 5.2). Table 5.2: Spearman’s rank order correlation analysis between domains of the WHOQoL-Bref Social Domains Physical Psychological Relationships Environment Physical 0.74** 0.54*** 0.63 Psychological 0.74 ** 0.62 0.66 Social Relationships 0.54*** 0.62 0.65 Environment 0.63 0.66 0.65 ** Correlation is significant at the 0.01 level (2-tailed) *** Correlation is significant at the 0.001 level (2-tailed)

Figure 5.9: Spearman’s rank order correlation analysis showing significance between domains of the WHOQoL-Bref. ** Correlation is significant at the 0.01 level (2-tailed), *** Correlation is significant at the 0.001 level (2-tailed), ns = non-significant

5.3.4 Parametric tests i. Independent Samples t-tests

Females reported higher scores for physical health domain (M = 62.3268, SD = 10.95680), psychological health domain (M = 66.6071, SD = 11.71560) and social realtionships domain (M = 57.6587, SD = 13.71005) when compared to males; (M = 61.5510, SD = 11.24523), (M = 66.0000, SD = 13.13081) and (M = 55.2381, SD = 16.21517) respectively. Males reported higher scores for the environmental health domain (M = 63.3571, SD = 11.59904) when compared to females (M = 63.0060, SD = 9.78267). From Table 5.3, there was no significant association between the quality of life domains for both males and females (p > 0.05). Gynecological cancer patients reported slightly higher scores for physical health domain, psychological health domain and environmental health domain when compared to breast cancer patients. Patients with breast cancer on the other hand reported higher scores for the social relationships domain when compared to gynecological cancer patients (Figure 5.3). From Figure 5.3, there was no significant association between the quality of life domains for both breast and gynaecological cancer patients (p > 0.05).

Table 5.3: Independent Samples t-test between domains of the WHOQoL-Bref and gender of patients Transformed QoL Male (M) Female t df p-value domains (M)

Physical health 61.5510 62.3268 -0.379 200 0.705 Psychological health 66.0000 66.6071 -0.273 201 0.785 Social Realtionships 55.2381 57.6587 -0.920 201 0.359 Environmental health 63.3571 63.0060 0.187 201 0.852 M = mean df = degrees of freedom QoL = quality of life

Figure 5.10: Independent Samples t-test for domains of the WHOQoL-Bref for breast and gynecological cancer patients. Bars represent means and standard deviations

ii. One-way ANOVA Non-opioids (M = 9.6875, SD = 1.57982) were most prescribed for patients with social relationships domain issues followed by weak opioids (M = 8.6000, SD = 2.40832) and strong opioids (M = 8.2500, SD = 1.64473). One-way ANOVA analysis revealed that there was significant between-groups differences; F(2, 58) = 4.125, p = 0.021 (Table 5.4). Post-hoc comparisons using the Tukey‘s HSD test showed that significant differences occurred between patients who were prescribed non-opioids and strong

opioids (p < 0.05) (Appendix 12). However, there was no significant differences between weak opioids and strong opioids (p > 0.05). Table 5.4: One-way ANOVA analysis of quality of life domains and groups of prescribed analgesics QoL domains Type of M SD df F p-value analgesic Physical health Non-opioids 21.5000 2.78089 2 0.322 0.726 Weak opioids 20.0000 4.84768 58 Strong opioids 21.0750 3.81890 Psychological Non-opioids 19.5000 3.94968 2 0.459 0.634 health Weak opioids 21.0000 2.64575 58 Strong opioids 19.5500 3.04623 Social Non-opioids 9.6875 1.57982 2 4.13 0.021 Realtionships Weak opioids 8.6000 2.40832 58 Strong opioids 8.2500 1.64473 Environmental Non-opioids 25.5000 2.70801 2 1.467 0.239 health Weak opioids 23.0000 4.41588 58 Strong opioids 24.5250 2.87329 df= degrees of freedom QoL = quality of life M = mean SD = standard deviation

iii. Multiple regression analysis The model did not reach significance as it did not successfully predict overall quality of life patients [F(7, 12) = 1.346, p = 0.310] (Table 5.5). The model explained 44% of variance in overall quality of life of patients. Patients‘ overall quality of life was not predicted by the predictors under consideration; age in years (β = 0.124, t = 0.427, p > 0.05), parity (β = -0.526, t = -2.151, p > 0.05), age at menarche (β = 0.352, t = 1.321, p > 0.05), days between interview and date of diagnosis (β = 0.235, t = 0.621, p > 0.05), days between consultation and first visit (β = 0.111, t = 0.471, p > 0.05), days between treatment initiation and first visit (β = 0.045, t = 0.125, p > 0.05) and state of health (β = 0.451, t = 1.822, p > 0.05) (Table 5.6). For every increase in age by 1 year, overall quality of life of patients increased by 12. For every increase in parity by 1 child, overall quality of life of patients decreased by 53. For every increase in age at menarche by 1 year, overall quality of life of patients increased by 35. For every increase in number of days between interview and diagnosis by 1 day, overall quality of life of patients increased by 24. For every

increase in number of days between consultation and first visit by 1 day, overall quality of life of patients increased by 11. For every increase in number of days between treatment initiation and first visit by 1 day, overall quality of life of patients increased by 45. For every increase in the state of health by 1, overall quality of life of patients increased by 45 (Table 5.6). Table 5.5: One-way ANOVA analysis for overall quality of life (dependent variable) and independent variables Modelb Df F Sig. 1 Regression 7 1.346 0.310b Residual 12 Total 19 R2 = 0.440 df= degrees of freedom` R2 = multiple correlation coefficient of determination b= predictors [(constant), state of health, age in years, number of days between treatment intiation and first visit, number of days between consultation and first visit, parity, age at menarche, number of days between interview and date of diagnosis]

Table 5.6: Regression coefficientsa Model B Β t Sig. 1 (Constant) -17.250 -0.329 0.748 Age in years 0.136 0.124 0.427 0.677 Parity -3.714 -0.526 -2.151 0.053 Age at menarche 3.400 0.352 1.321 0.211 Number of days between interview and 0.061 0.235 0.621 0.546 date of diagnosis Number of days between consultation 0.140 0.111 0.471 0.646 and first visit Number of days between treatment 0.024 0.045 0.125 0.903 intiation and first visit State of health 13.886 0.451 1.822 0.093 a = dependent variable: Overall QoL (Transformed) β = Beta coefficient Sig = p-value B = multiple regression coefficient

iv. Multiple linear regression equation Overall quality of life of patients = -17.250 + 0.136 (age) - 3.714 (parity) + 3.400 (age at menarche) + 0.061 (number of days between interview and date of diagnosis) + 0.140 (number of days between consultation and first visit) + 0.024 (number of days between treatment initiation and first visit) + 13.886 (state of health)

5.4 Discussion Generally, patients had moderate overall quality of life (M = 3.1647, SD = 0.11075); an observation supported by Gholami et al. (2016). This implies that good quality of life interventions can enhance the quality of life of these patients and improve their QALYs.

All patients (95.2%) who reported being restricted from doing what they wanted to do by cancer pain should undergo comprehensive pain assessment and prompt management of cancer pain initiated in accordance with the tenets of the WHO analgesic ladder (Dalton and Youngblood, 2000). Where they are refractory to prescribed analgesics, other interventional pain management approaches like the use of nerve blocks may be helpful for these patients. Where these patients undergo complete remission of their cancer, they should be cautioned that they may experience ocassional cancer pain as observed by Russell et al. (2014).

All patients (99.0%) who needed medical treatments to function in their dialy lives may benefit from regular review of their anticancer treatments. This may ensure complete cancer remission and also prevent cancer pain as observed by Al Qadire et al. (2013). Introduction of dietary and lifestyle changes, alternative medicine, complementary therapy, improved sleep and stress management may be beneficial to these patients as it can increase their energy levels and enable them to perform their ADLs/ IADLs. Additionally, these patients can benefit from adequate support from family and friends to be able to accomplish their ADLs/ IADLs. Where they are refractory to these approaches, psychostimulants like methylphenindate and modafinil may be helpful for these patients as documented by Al Qadire et al. (2013).

Patients (79.9%) who did not get around well may benefit from mobility aids like wheelchairs, canes, quad canes, forearm crutches, walkers and Personal Mobility Vehicles (PMVs) as documented by Mishra et al. (2012).

Patients who were dissastisfied with their sleep (55%) could be suffering from sleep disorders (eg. insomnia and abnormal circadian rhythm) which can cause anxiety and depression in cancer patients as observed by Mishra et al. (2012). Drugs like hormonal therapy, corticosteroids and antidepressants are known to cause sleep disorders in cancer patients (Gunn et al., 2013). Therefore the prescriptions of these patients should be critically reviewed to ensure that they are not prescribed these drugs unless very necessary. These patients could be helped by assessing them for non-malignant

conditions such as snoring and headache; as these conditions could increase their chances of developing sleep disorders. When these patients are well-rested, they can have improved energy levels which can help them cope better with the effects of anticancer treatments. Perhaps, cognitive behavioural therapy (CBT) approaches like relaxation therapy may reduce anxiety and improve sleep in these patients. Moreover, non-drug approaches like diet, regular exercise, regular bowel and bladder habits in addition to treatment of possible underlying conditions which cause sleep disorders (eg. pain and depression), may promote sleep and improve the quality of life of these patients.

Patients who were dissatisfied with their ability to perform their ADLs (66.9%) should be re-examined, their performance status documented and their anticancer treatments evaluated. These patients may have disabilities (eg. amputations) and may require physical support with performing their ADLs/ IADLs; especially from family and friends. The use of assisted toileting aids like bedside commodes and bedpans may be helpful to these patients. They may also benefit from the use of mobility aids like wheelchairs.

Patients who were dissatisfied with their capacity for work (69.7%) can be helped by asking them to reduce their working hours, or encouraging them to work flexible hours or work on part-time basis until they fully recover. The employers of these patients should be engaged in this agreement as legally required. Additionally, these patients may benefit from dietary and lifestyle changes as well as professional help (eg. physiotherapists, occupational therapists and rehabilitation specialists). This can increase their motivation and vigor and help in the accomplishment of required tasks and fully get back to work.

Patients who did not enjoy life (70.5%), could not concentrate (60.8%), had problems with their self-esteem (54.7%), felt their life was not meaningful (45.1%), had body image acceptance issues (75.5%) and experienced negative feelings; blue mood, despair, anxiety and depression (29.9%) may benefit from emotional support from family, friends, spouses/ partners, other cancer survivors, counsellors or could turn to their faith to help them cope. Strategies like complementary therapy, laughter and creative outlets (eg. art, music and dance) could relieve the stress, depression, loneliness and anger associated with cancer diagnosis and could benefit these patients.

Additionally, cosmetic aids (like wigs and mastectomy bras) may help these patients to regain their confidence and self-esteem.

Patients who were dissastisfied with their sex lives (71.4%) may be struggling from disfigured body images (eg. amputations), emotional changes (eg. depression and fear) and changes in their sex drive or libido caused by side effects of anticancer treatments (eg. hormonal therapy and pelvic radiotherapy). In the absence of sexual intercourse, hugging, touching and holding, cuddling and oral sex may be helpful to these patients as it can foster closeness to their partners or spouses. Moreover, these patients could benefit from professional advice from health care providers, sex therapists, clinical psychologists and counsellors. For male patients, complementary therapy, oral drugs (eg. sildenafil and tadalafil), penile injections (eg. intraurethral alprostadil and intracavernous papaverine), penile implants, vacuum constriction devices and surgery (eg. penile prosthesis) could be helpful. For female patients, pelvic floor exercises, vaginal lubricants and vaginal dilators could help.

Patients who were dissatisfied with: the support they got from friends (71.4%) and their personal relationships (72.4%) could experience compounded psychosocial problems which can worsen their cancer pain experience as observed by Las Hayas et al. (2015). This is because friends, family, neighbours, and church group members could act as patients‘ informal social supports and can provide enough emotional, informational and logistical support as affirmed by Las Hayas et al. (2015).

Patients who felt their physical environment was not healthy (54.4%) could benefit from good personal hygiene practices (eg. good hand washing habits) and environmental hygiene practices (eg. use of latex gloves and antibacterial agents for cleaning surfaces). Additionally, avoidance of: crowded or dirty places and exposure to other sick people may improve the wellbeing of these patients.

Patients who did not have enough money to make ends meet (61.1%) could struggle with the high costs of cancer treatments. This could be compounded by the possibility of low incomes, job losses and lack of health insurance which can plunge these patients and their families into debts which can make the payment of rent, utilities, transportation and food a huge challenge as previously observed by Lekka et al. in 2014.

Patients who did not have access to the information they needed in their day-to-day lives (71.1%) should be provided with detailed information about the type, stage, treatment options, prognosis, rehabilitation and coping strategies of cancer by health care providers. This cancer-related information provided by health care providers could be possibly tailor-made to meet the expectations and preferences of these patients on an advance rather than on an as-needed bases.

Patients who did not have opportunity for leisure activities (72.5%) could be encouraged to participate in activities such as their hobbies (eg. reading and painting).

Patients who were dissatisfied with the conditions of their living places (52.7%) could benefit from reduction in noise pollution, increase in ventilation and practice of aromatherapy. Also, patients who were dissatisfied with their means of transportation (59.6%) may skip hospital appointments or pharmacy medication pick-ups in the absence of available and affordable means of transportation to treatment facilities. Policies by governmental agencies, non-governmental agencies (NGOs), faith based groups and hospital authorities that could cater for the transportation costs and accommodation challenges of these patients can ease this burden on these patients.

The average interview completion time; 15 minutes (SD = 6.32) of patients in this study was longer than that reported by other researchers (Nedjat et al., 2008; Chien et al., 2009). This was probably because of the face-to-face interview approach adopted in the current study instead of the self-completion approach. Also the fact that majority of patients had no formal education coupled with the fact that some items on the WHOQoL-Bref were culturally sensitive could have contributed to the relatively long average interview completion time. Furthermore, very ill patients; ECOG performance status 3 to 4 (7%) were likely to need more time to complete the interviews.

Patients who did not admit being ill (26.7%) inspite of their cancer diagnosis could probably be suffering from the ‗it is well with my soul‘ syndrome in Africans which has been predicted by Colbourn et al. (2012) to play a vital part in patients‘ perception of their health statuses.

Sex enhancing interventions that could better the sex lives of patients who were dissastisfied are essential as this item had the lowest satisfaction rating (M = 2.563, SD = 1.1536). The fact that discussions on sex and sexuality are deemed culturally unacceptable in the Ghanaian society could have contributed to this. The item asking

about patients‘satisfaction with their sex lives needs to be critically examined to determine its appropriateness on the WHOQoL-Bref as corroborated by Krageloh et al. (2011), Gholami et al. (2016) and Mazaheri (2010).

The excellent internal consistency reliability of the study instrument; (α = 0.91) has been affirmed by these researchers; Gholami et al. (2016), Castro et al. (2007), Agnihotri et al. (2010), Halvorsrud et al. (2008), Mazaheri (2010) and Krageloh et al. (2011).

5.5 Conclusion

Patients had moderate quality of life.

5.6 Recommendations

1. Governmental agencies, faith-based groups and non-governmental organisations can provide adequate transportation support services for cancer patients like free or discounted passes or vouchers for the metro mass transit system, taxis and other modes of transportation in the country.

2. Adequate written information should be made readily available to all cancer patients.

3. Comprehensive health care for cancer patients should be planned and evaluated by multidisciplinary cancer teams.

CHAPTER SIX

6.0 GENERAL DISCUSSION Cancer is emerging as a major public health issue in Sub-Saharan Africa and other Low-and Middle-Income Countries (LMIC) due to increasing prevalence of important disease associated risk factors (Benedetti et al., 2009; Adesina et al., 2013). A high residual burden of infectious agents (HIV/ AIDS, HPV, HBV), increased tobacco use and alcohol consumption, increased obesity and sedentary lifestyle, increased consumption of carcinogenic foods and drinks, excessive exposure to UV radiation and pollutants like polychlorinated biphenyls (PCBs) drives the rate of certain cancers. This is particularly worrying as communicable diseases like HIV/ AIDS are endemic in Sub- Saharan Africa and pose a huge health challenge. The psychosocial and physical ramifications of cancer are serious, long lasting, can adversely alter the quality of life of patients and wreak more havoc than any other disease (Dankert et al., 2003).

The cost of cancer treatments is rising because modern technological advances which enable patients live longer are expensive and the patient usually bears a large portion of the overall cost of treatment (Hollard, 2002; Wang et al., 2012). This is particularly challenging as in Ghana, the NHIA coverage is limited to a few cancers. Additionally, there is lack or inadequate cancer diagnostic and screening equipment in oncology directorates in most hospitals in Sub-Saharan Africa (Adesina et al., 2013). The scanty equipment available which are in good working conditions, are usually antiquated and less effective (Adesina et al., 2013). Also, there is shortage of trained oncologists and other health care personnel in Sub-Saharan Africa which makes caring for the rising numbers of cancers cases daunting. This is coupled with unsteady supply of electricity and water.

Over the years, the major public development and health organizations like the World Bank, WHO and private donors like the Bill and Melinda Gates Foundation have chanelled their funding towards infectious diseases in Sub-Saharan Africa (McCoy et al., 2009). But investing additional funds in cancer care in Sub-Saharan Africa will not only save lives but develop the economy because working-class patients can obtain total care and lead productive lives. Fortunately pragmatic initiatives are being put in place to tackle these challenges and rid Sub-Saharan Africa of avoidable and preventable deaths associated with cancer (Adesina et al., 2013; Coleman, 2014; Coleman et al., 2014). For instance, cancer professionals have launched an International Cancer Expert Corps

(ICEC) to link medical experts in better off countries with those in poor countries (Coleman, 2014; Coleman et al., 2014). Also, private companies have launched an initiative supported by the White House, USA to use smartphones and other readily accessible technologies in poorer countries to improve diagnostic and screening procedures (Adesina et al., 2013). For instance, the partnership between the University of Pennslyvannia Perelman School of Medicine and Botswana in which health care providers can take cellphone pictures of a patient‘s cervix for remote analysis by gynaecologists (DeRenzi et al., 2011); has recently been adopted in Ghana. Hopefully if other Low-and Middle-Income Countries adopt this strategy, it may lessen the sociocultural barriers associated with cancer screening and save lives by fostering early detection. Also if chemotherapeutic agents and pain medications could be disseminated to all hospitals and clinics in Ghana and made free of charge for all cancer patients, it would foster adherence.

Given the magnitude of the current and anticipated future challenges associated with cancer, these initiatives are not enough. Conserted efforts from governments in developing countries, the major international development organizations and major private health care donors is needed to tackle cancer in poor countries.

Creating a solid picture of a cancer patient‘s health and functional well-being requires an amalgamation of accurate and comprehensive medical history, pain assessment and quality of life assessment (Abruquah et al., 2017). Good communication is the bedrock of effective medical history taking, as well as pain and quality of life assessments. It is also a prerequisite for effective cancer pain diagnoses and efficient management (DeVoe et al., 2009).

A medical interview is an important manifestation of effective communication between patients and healthcare providers which reveals the patients‘ ideas, concerns and expectations about their diagnosis. Good communication being the foundation for positive patient-healthcare provider relationship can enhance patient satisfaction and lead to better treatment compliance (DeVoe et al., 2009). Effective communication can be hampered by the physical setting of a medical interview (Giardini et al., 2011). An environment which allows privacy and confidentiality is important as it allows the patient to share useful information, thoughts and concerns.

The content of a medical history varies depending on the patient‘s presenting compliant, history of presenting compliant, past medical history, drug and family history of cancer, social history and systems review (Hickey, 2010). The focus of a medical history is to elicit relevant information that would aid cancer and cancer pain diagnosis and treatment. The commonest approach to patient‘s history taking is a medical interview but a referral letter or patient‘s medical notes can also be used.

Clinical assessment of acute cancer pain is relatively simple and straightforward. Usually, assessment of pain location, temporal pattern and intensity often suffices (Hickey, 2010). Although the BPI does not assess acute cancer pain, some recently invented pain assessment tools do. Examples are Back Pain Function Scale (BPFS), Breast Cancer Treatment Outcomes Scale (BCTOS), Edmonton Functional Assessment Tool-2 (EFAT-2) and Medical Outcome Study 116 item core set (MOS- 116) (Kaasa and Wessel, 2001; Busija et al., 2011).

Additionally assessment of indices such as pain at rest, dynamic pain, baseline pain and neuropathic pain with acute cancer pain assessment tools are very relevant (Zeitoun et al., 2013). Assessment of acute cancer pain at rest is particularly important after surgery to ensure that the patient is comfortable in bed, to prevent the development of chronic hyperalgesic pain and to select optimum pain management strategies. But dynamic pain assessment during deep breathing, coughing and mobilization is much more important for reducing the risks of thromboembolic and cardiopulmonary complications associated with surgery as well as the selection of optimum pain management strategies. Assessment of baseline pain before analgesia helps in the documentation of analgesic assay sensitivities. The possibility of CNS remodelling after surgery which can lead to the development of chronic neuropathic pain syndromes, necessitates the assessment of neuropathic pain.

Regular and comprehensive assessments of chronic cancer pain syndromes should involve documenting pain history, physical examination and specific diagnostic tests. Cancer pain history should clarify pain location(s), intensity, pain descriptors, temporal patterns, possible pathophysiological and etiological issues. Cancer patient‘s physical examination includes neurological examination, musculoskeletal system examination, assessment of psychological factors and specific diagnostic tests like computerized tomography (CT/ CAT) and magnetic resonance imaging (MRI).

100

Comprehensive cancer pain assessment tools including the BPI, McGill Pain Questionnaire and its short form (SF-MPQ), Massachusetts General Hospital Pain Center‘s Pain Assessment Form and Pain Quality Assessment Scale (PQAS); although are good chronic pain assessors were invented some years ago which makes them antiquated. Recently, comprehensive cancer pain assessment tools have been invented for chronic cancer pain assessment. Examples are Regional Pain Scale (RPS), Cognitive Risk Profile (CRP), Oswestry Disability Index 2 (OSW-2), Pain assessment form (PAF), Leeds assessment of neuropathic symptoms and signs (LANSS), Medication Assessment Tool for Cancer Pain Management (MAT-PC) and Pain Opioid Analgesics Beliefs Scale-Cancer (POABS-CA) (Busija et al., 2011; Copay et al., 2008). These pain scales may neglect the emotional components of pain and symptoms of mental distress like depression, anxiety and stress. Due to this, the Indiana Polyclinic Combined Pain Scale (IPCPS) which combines the assessment domains of the various scales: pain history, functional interference, depression and anxiety may cover areas that the aforementioned pain scales may not perfectly measure (Busija et al., 2011; Dworkin et al., 2004).

Although the Brief Pain Inventory is the most commonly used cancer pain assessment tool, it has a myriad of shortfalls (Ballout et al., 2011). Fortunately, these shortfalls can be adressed by other pain assessment tools. For example, the BPI does not ask about the radiation and referral of cancer pain; the Regional Pain Scale can be helpful in this regard. The Brief Pain Inventory does not ask about the onset, duration and temporal pattern of cancer pain; the Leeds assessment of neuropathic symptoms and signs can be helpful in this regard. Also, cancer pain intensity at rest and with movements is not addressed by the BPI; the Regional Pain Scale and the pain assessment form can be useful in this instance. Additionally, the Brief Pain Inventory does not assess the medical and psychiatric comorbidities of cancer patients; the Indiana Polyclinic Combined Pain Scale can be helpful in this regard. The Brief Pain Inventory does not access the doses of prescribed analgesics and drug allergy of cancer patients; the Medication Assessment Tool for Cancer Pain Management can be useful in this instance.

Cancer patients on long-term opioid therapy due to chronic cancer pain are likely to show addition potentials. Although the Brief Pain Inventory has one item on potential opioid addiction, pain assessment tools designed specifically for evaluating potential

101

aberrant behaviours of starting long-term opioid therapy like the Screening Tool for Addiction Risk (STAR) can be used to assess possible opioid addiction in cancer patients (Dworkin et al., 2004). The POABS-CA can also be used to assess patients‘ myths associated with long-term opioid use (Dworkin et al., 2004). The Initiative on Methods, Measurement and Pain Assessment in Clinical Trials (IMMPACT) recommendations advocate that pain clinics evaluate the outcomes of clinical pain management in a standardized manner (Dworkin et al., 2004). In follow-up consultations, patients self-reported ratings of the betterment or worsening of pain should be assessed using tools like the patient global impression of change scale (Dworkin et al., 2004).

Important quality of life domains not assessed by the WHOQoL-Bref include spiritual well-being, biological, human/ legal rights, extent of anxiety/ depression and national health insurance coverage of cancer patients. Fortunately, a plethora of quality of life assessment tools exit which can cover some aspects of these missing domains of the World Health Organization Quality of life-Brief version. These include the Quality of Life Scale (QOLS), Wisconsin Quality of Life Index (W-QLI), the Nottingham Health Profile (NHP), the EuroQoL Quality of Life Scale (EQ-5D), the Health Utilities Index (HUI-3), the Hospital Anxiety and Depressive Scale (HADS) and the Support Team Assessment Schedule (STAS) (Dworkin et al., 2004).

Based on the outcomes obtained in this study, it can be said that medical history taking was not comprehensive. Also, the pain assessment or quality of life assessment tools under consideration were not ideal. Although, several pain and quality of life assessment tools can be used, it is critical to use assessment tools that are psychometrically sound.

6.1 Limitations of the study

The study design used in this study precludes the assessment of the study instruments‘ responsiveness to change and thus test-retest reliability analysis of the instruments could not be carried out. The Brief Pain Inventory-Long Form is too lengthy for repeated use in the clinical settings for cancer pain assessments. This study did not add a qualitative dimension that could probe deeper into the psychological ability of patients to give a detailed account of their condition. The face-to-face interview approach

102

adopted in this study may have lead to information bias and poor psychometric properties of the study instruments. The convenient sampling technique employed in this study could have lead to selection bias due to non response or voluntary response.

103

CHAPTER SEVEN

7.0 CONCLUSIONS AND RECOMMENDATIONS 7.1 Conclusions This study presents some findings on cancer pain: assessement, management and effect on the quality of life of patients at the Komfo Anokye Teaching Hospital in Ghana. The study revealed that: 1. Comprehensive pain assessments were not done for patients. 2. Pain relief was suboptimal in some patients. 3. Patients had moderate overall quality of life.

7.2 Recommendations for future research: Given the infinite nature of knowledge, it is not out of place that areas which still need to be explored are highlighted to provide better and more holistic understanding of the novel and significant findings revealed in this study. These knowledge gaps could be filled by: 1. Repeating the study in other teaching hopitals in the country using a larger population of patients so that the results obtained can be generalized for all oncology patients in Ghana. 2. Undertaking comprehensive pain assessments at all cancer centres in the country to ensure effective pain management in patients. 3. The use of Brief Pain Inventory-Short Form which is concise must be encouraged at all cancer centres in the country.

104

REFERENCES Abotchie PN and Shokar NK (2009) Cervical cancer screening among college students in Ghana: knowledge and health beliefs. International journal of gynecological cancer: official journal of the International Gynecological Cancer Society 19(3); 412.

Abruquah AA, Biney RP, Osei-Bonsu EB, Boamah KM and Woode E (2017) Adequacy of pain management in oncology patients at a tertiary hospital in Ghana. International Journal of Basic & Cinical Pharmacology 6(2); 251-256.

Adesina A, Chumba D, Nelson AM, Orem J, Roberts DJ, Wabinga H et al. (2013) Improvement of pathology in sub-Saharan Africa. The lancet oncology 14(4); e152-e157.

Aghillinejad M, Mokamelkhan EA, Imanizadeh Z and Danesh H (2016) Occupational Class Groups as a Risk Factor for Genitourinary Cancer-A Matched Case- Control Study. Iranian Journal of Health, Safety and Environment 3(1S); 483- 488.

Agnihotri K, Awasthi S, Chandra H, Singh U and Thakur S (2010) Validation of WHO QOL-BREF instrument in Indian adolescents. The Indian Journal of Pediatrics 77(4); 381-386.

Ahlner-Elmqvist M, Jordhoy MS, Bjordal K, Jannert M and Kaasa S (2008) Characteristics and quality of life of patients who choose home care at the end of life. Journal of pain and symptom management 36(3); 217-227.

Ahmedzai SH, Leppert W, Janecki M, Pakosz A, Lomax M, Duerr H et al. (2015) Long-term safety and efficacy of oxycodone/ naloxone prolonged-release tablets in patients with moderate-to-severe chronic cancer pain. Supportive Care in Cancer 23(3); 823-830.

Ahrensberg JM, Hansen RP, Olesen F, Schroder H and Vedsted P (2012) Presenting symptoms of children with cancer: a primary-care population-based study. British Journal of General Practice 62(600); e458-e465.

Aisyaturridha A, Naing L and Nizar AJ (2006) Validation of the Malay Brief Pain Inventory questionnaire to measure cancer pain. Journal of pain and symptom management 31(1); 13-21.

Al Qadire M, Tubaishat A and Aljezawi MW (2013) Cancer pain in Jordan: prevalence and adequacy of treatment. International journal of palliative nursing 19(3); 125-130.

Alifrangis C, Koizia L, Rozario A, Rodney S, Harrington M, Somerville C, Peplow T and Waxman J (2011) The experiences of cancer patiients. QJM: An International Journal of Medicine 104(12); 1075-1081.

Al-Kindi SG, Zeinah GFA and Hassan AA (2014) Palliative care knowledge and attitudes among oncology nurses in Qatar. American Journal of Hospice and Palliative Medicine 31(5); 469-474.

105

Amdal CD, Jacobsen AB, Tausjo JE, Wiig JN, Waarloe T, Sandstad B et al. (2011) Palliative interventions and prognosis in patients with advanced esophageal cancer. Diseases of the Esophagus 24(7); 502-509.

Anast JW, Sadetsky N, Pasta DJ, Bassett WW, Latini D, DuChane J et al. (2005) The impact of obesity on health related quality of life before and after radical prostatectomy (data from CaPSURE). The Journal of urology 173(4); 1132- 1138.

Andreassen TN, Klepstad P, Davies A, Bjordal K, Lundstrom S, Kaasa S et al. (2012) Is oxycodone efficacy reflected in serum concentrations? A multicenter, cross- sectional study in 456 adult cancer patients. Journal of pain and symptom management 43(4); 694-705.

Anton A, Montalar J, Carulla J, Jara C, Batista N, Camps C et al. (2012) Pain in clinical oncology: patient satisfaction with management of cancer pain. European journal of pain 16(3); 381-389.

Antoniou A, Phroah PDP, Narod S, Risc HA, Eyfjord JE, Hopper JL et al. (2003) Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combines analysis of 22 studies. The American Journal of Human Genetics 72(5); 1117- 1130.

Arcidiacono PG, Calori G, Carrara S, McNicol ED and Testoni PA (2011) Celiac plexus block for pancreatic cancer pain in adults. The Cochrane database of systematic reviews CD007519-CD007519.

Argiris A, Karamouzis MV, Raben D and Ferris RL (2008) Head and neck cancer. The Lancet 371(9625); 1695-1709.

Asnani MR, Lipps GE and Reid ME (2009) Utility of WHOQOL-BREF in measuring quality of life in sickle cell disease. Health and quality of life outcomes 7(1); 75.

Atkinson TM, Halabi S, Bennett AV, Rogak L, Sit L, Li Y et al. (2012) Measurement of affective and activity pain interference using the Brief Pain Inventory (BPI): Cancer and Leukemia Group B 70903. Pain Medicine 13(11); 1417-1424.

Atkinson TM, Mendoza TR, Sit L, Passik S, Scher HI, Cleeland C et al. (2010) The Brief Pain Inventory and its "pain at its worst in the last 24 hours" item: clinical trial endpoint considerations. Pain Medicine 11(3); 337-346.

Awadalla AW, Ohaeri JU, Tawfiq AM and. Al-Awadi SA (2006) Subjective quality of life of outpatients with diabetes: comparison with family caregivers' impressions and control group. Journal of the National Medical Association 98(5); 737.

Awasthi S, Agnihotri K, Chandra H, Singh U and Thakur S (2012) Assessment of Health-Related Quality of Life in school-going adolescents: validation of PedsQL instrument and comparison with WHOQOL-BREF. National Medical Journal of India 25(2); 74-79.

106

Ballout S, Noureddine S, Huijer HA and Kanazi G (2011) Psychometric evaluation of the arabic brief pain inventory in a sample of Lebanese cancer patients. Journal of pain and symptom management 42(1); 147-154.

Batalha LMC and Mota AA (2013) Massage in children with cancer: effectiveness of a protocol. Journal de pediatria 89(6); 595-600.

Beauregard L, Pomp A and Choiniere M (1998) Severity and impact of pain after day- surgery. Canadian Journal of Anaesthesia 45(4); 304.

Benedetti A, Parent ME and Siemiatycki J (2009) Lifetime consumption of alcoholic beverages and risk of 13 types of cancer in men: Results from a case control study in Montreal. Cancer detection and prevention 32(5-6); 352-362.

Bircher J (2005) Towards a dynamic definition of health and disease. Medicine, Health Care and Philosophy 8(3); 335-341.

Black B, Herr K, Fine P, Sanders S, Tang X, Bergen-Jackson K et al. (2011) The relationships among pain, nonpain symptoms, and quality of life measures in older adults with cancer receiving hospice care. Pain Medicine 12(6); 880-889.

Bolarinwa OA (2015) Principles and methods of validity and reliability testing of questionnaires used in social and health science researches. Nigerian postgraduate medical journal 22(4); 195-201.

Bortsov AV, Platts-Mills TF, Peak DA, Jones JS, Swor RA, Domeier RM et al. (2014) Effect of pain location and duration on life function in the year after motor vehicle collision. Pain 155(9); 1836-1845.

Breitbart W, Passik S, McDonald MV, Rosenfeld B, Smith M, Kaim M et al. (1998) Patient-related barriers to pain management in ambulatory AIDS patients. Pain 76(1-2); 9-16.

Bruix J and Sherman M (2011) Management of hepatocellular carcinoma: an update. Hepatology 53(3); 1020-1022.

Brune K, Abe T, Canto M, O‘Malley L, Klein AP, Maitra A et al. (2006) Multifocal neoplastic lesions associated with lobular atrophy of the pancreas in patients having a strong family history of pancreatic cancer. The American journal of surgical pathology 30(9); 1067.

Busija L, Pausenberger E, Haines TP, Haymes S, Buchbinder R and Osborne RH (2011) Adult measures of general health and health-related quality of life: Medical Outcomes Study Short Form 36-Item (SF-36) and Short Form 12 (SF- 12) Health Surveys, Nottingham Health Profile (NHP), Sickness Impact Profile (SIP), Medical Outcomes Study Short Form 6D (SF-6D), Health Utilities Index Mark 3 (HUI3), Quality of Well-Being Scale (QWB), and Assessment of Quality of Life (AQOL). Arthritis care & research 63(S11); S383-S412.

Buyukyilmaz F and Asti T (2013) The effect of relaxation techniques and back massage on pain and anxiety in Turkish total hip and knee arthroplasty patients. Pain Management Nursing 14(3); 143-154. 107

Caffo O, Sava T, Comploj E, Fariello A, Zustovich F, Segati R et al. (2011) Impact of docetaxel-based chemotherapy on quality of life of patients with castration- resistant prostate cancer: results from a prospective phase II randomized trial. BJU International 108(11); 1825-1832.

Campisi J (2001) Cellular senescence as a tumor-suppressor mechanism. Trends in cell biology 11(11); S27-S31.

Campisi J (2005) Suppressing cancer: the importance of being senescent. Science 309(5736); 886-887.

Caraceni A (2001) Evaluation and assessment of cancer pain and cancer pain treatment. Acta Anaesthesiologica Scandinavica 45(9); 1067-1075.

Caraceni A and Portenoy RK (1999) An international survey of cancer pain characteristics and syndromes. IASP Task Force on Cancer Pain. International Association for the Study of Pain. Pain 82(3); 263-274.

Caraceni A, Cherny N, Fainsinger R, Kaasa S, Poulain P, Radbruch L et al. (2002) Pain measurement tools and methods in clinical research in palliative care: recommendations of an Expert Working Group of the European Association of Palliative Care. Journal of pain and symptom management 23(3); 239-255.

Castel LD, Abernethy AP, Li Y, DePuy V, Saville BR and Hartmann KE (2007) Hazards for pain severity and pain interference with daily living, with exploration of brief pain inventory cutpoints, among women with metastatic breast cancer. Journal of pain and symptom management 34(4); 380-392.

Castro MG, Oliveira MS, Miguel AC and Araujo RB (2007) WHOQOL-BREF psychometric properties in a sample of smokers. Revista Brasileira de Psiquiatria 29(3); 254-257.

Chapman S (2012) Cancer pain part 2: assessment and management. Nursing Standard (through 2013) 26(48); 44.

Chien CW, Wang JD, Yao G, Hsueh IP and Hsieh CL (2009) Agreement between the WHOQOL-BREF Chinese and Taiwanese versions in the elderly. Journal of the Formosan Medical Association 108(2); 164-169.

Chiu WT, Huang SJ, Hwang HF, Tsauo JY, Chen CF, Tsai SH et al. (2006) Use of the WHOQOL-BREF for evaluating persons with traumatic brain injury. Journal of Neurotrauma 23(11); 1609-1620.

Clark MJ, Harris N, Griebsch I, Kaschinski D and Copley-Merriman C (2014) Patient- reported outcome labeling claims and measurement approach for metastatic castration-resistant prostate cancer treatments in the United States and European Union. Health and quality of life outcomes 12(1); 104.

Cleeland CS and Ryan KM (1994) Pain assessment: Global use of the Brief Pain Inventory. Annals, Academy of Medicine, Singapore 23(2); 129-138.

108

Clegg-Lamptey J, Dakubo J and Attobra Y (2009) During treatment in Ghana? A pilot study. Ghana medical journal 43(3).

Colbourn T, Masache G and Skordis-Worrall J (2012) Development, reliability and validity of the Chichewa WHOQOL-BREF in adults in Lilongwe, Malawi. BMC research notes 5(1); 346.

Coleman CN (2014) Bringing cancer care to the underserved globally: A challenging problem for which radiation oncology can pioneer novel solutions. International Journal of Radiation Oncology. Biology. Physics 89(3); 443-445.

Coleman CN, Formenti SC, Williams TR, Petereit DG, Soo KC, Wong J et al. (2014) The international cancer expert corps: a unique approach for sustainable cancer care in low and lower-middle income countries. Frontiers in oncology 4; 333.

Conaghan PG, Peloso PM, Everett SV, Rajagopalan S, Black CM, Mavros P et al. (2015) Inadequate pain relief and large functional loss among patients with knee osteoarthritis: evidence from a prospective multinational longitudinal study of osteoarthritis real-world therapies. Rheumatology 54(2); 270-277.

Consedine NS, Magai C, Krivoshekova YS, Ryzewicz L and Neugut AI (2004) Fear, anxiety, worry and breast cancer screening behavior: a critical review. Cancer Epidemiology and Prevention Biomarkers 13(4); 501-510.

Cook KF, Schalet BD, Kallen MA, Rutsohn JP and Cella D (2015) Establishing a common metric for self-reported pain: linking BPI Pain Interference and SF-36 Bodily Pain Subscale scores to the PROMIS Pain Interference metric. Quality of Life Research 24(10); 2305- 2318.

Copay AG, Glassman SD, Subach BR, Berven S, Schuler TC and Carreon L (2008) Minimum clinically important difference in lumber spine surgery patients: a choice of methods using the Oswestry Disability Index, Medical Outcomes Study questionnaire Short Form 36, and Pain Scales. The Spine Journal 8(6); 968-974.

Couch FJ, Hart SN, Sharma P, Toland AE, Wang X, Miron P et al. (2015) Inherited mutations in 17 breast cancer susceptibility genes among a large triple negative breast cancer cohort unselected for family history of breast cancer. Journal of clinical oncology 33(4); 304.

Crew KD, Capodice JL, Greenlee H, Brafman L, Fuentes D, Awad D et al. (2010) Randomized, blinded, sham-controlled trial of acupuncture for the management of aromatase inhibitor-associated joint symptoms in women with early-stage breast cancer. Journal of Clinical Oncology 28(7); 1154-1160.

Cuff L, Fann J, Bombardier C, Graves D and Kalpakjian C (2014). Depression, pain intensity, and interference in acute spinal cord injury. Topics in spinal cord injury rehabilitation 20(1); 32-39.

Daher M (2011) Opioids for cancer pain in the Middle Eastern countries: a physician point of view. Journal of pediatric hematology/oncology 33; S23-S28.

109

Dalton JA and Youngblood R (2000) Clinical application of the World Health Organization analgesic ladder. Journal of Infusion Nursing 23(2); 118-124.

Dankert A, Duran G, Engst-Hastreiter U, Keller M, Waadt S, Henrich G et al. (2003) Fear of progression in patients with cancer, diabetes mellitus and chronic arthritis. Die Rehabilitation 42(3); 155-163.

Dassonneville L, Lansiaux A, Wattelet A, Wattez N, Mahieu C, Van Miert S et al. (2000) Cytotoxicity and cell cycle effects of the plant alkaloids cryptolepine and neocryptolepine: relation to drug-induced apoptosis. European journal of pharmacology 409(1); 9-18.

De Oliveira GS, Chang R, Khan SA, Hansen NM, Khan JH, McCarthy RJ et al. (2014) Factors associated with the development of chronic pain after surgery for breast cancer: a propective cohort from a tertiary center in the United States. The breast journal 20(1); 9-14.

Deandrea S, Montanari M, Moja L and Apolone G (2008) Prevalence of undertreatment in cancer pain. A review of published literature. Annals of oncology 19(12); 1985-1991.

DeAngelis LM, Seiferheld W, Schold SC, Fisher B and Schultz CJ (2002) Combination chemotherapy and radiotherapy for primary central nervous system lymphoma: Radiation Therapy Oncology Group Study 93-10. Journal of Clinical Oncology 20(24); 4643-4648.

Dekel BGS, Tomasi M, Vasarri A, Gori A, Adversi M, Castagnoli A et al. (2014) Opioid titration with sustained-release oxycodone and immediate-release morphine for moderate/ severe cancer pain: a pilot assessment of the CoDem protocol. Journal of opioid management 10(1); 29-38.

Delongchamps NB, de la Roza G, Chandan V, Jones R, Sunheimer R, Threatte G et al. (2008) Evaluation of prostatitis in autopsied prostates- is chronic inflammation more associated with benign prostatic hyperplasia or cancer? The Journal of urology, 179(5); 1736-1740.

DeRenzi B, Borriello G, Jackson J, Kumar VS, Parikh TS, Virk P et al. (2011) Mobile phone tools for field-based health care workers in low-income countries. Mount Sinai Journal of Medicine: A Journal of Translational and Personalized Medicine 78(3); 406-418.

DeVoe JE, Wallace LS and Fryer Jr GE (2009) Measuring patients‘ perceptions of communication with healthcare providers: Do differences in demographic and socioeconomic characteristics matter? Health Expectations 12(1); 70-80.

Donovan KA, Taliaferro LA, Brock CW and Bazargan S (2008) Sex differences in the adequacy of pain management among patients referred to a multidisciplinary cancer pain clinic. Journal of pain and symptom management 36(2); 167-172.

110

Droz J and Howard FM (2011) Use of the Short Form McGill Pain Questionnaire as a diagnostic tool in women with chronic pelvic pain. Journal of minimally invasive gynecology 18(2); 211-217.

Duncan K, Uwimpuhwe H, Czibere A, Sarkar D, Libermann TA, Fisher PB et al. (2012) NSAIDs induce apoptosis in nonproliferating ovarian cancer cells and inhibit tumor growth in vivo. IUBMB life 64(7); 636-643.

Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP et al. (2004) Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain 113(1); 9-19.

Edge SB (2010) AJCC cancer staging handbook: from the AJCC cancer staging manual. Springer 2010; 718.

Eggermont LHP, Bean JF, Guralnik JM and Leveille SG (2009) Comparing pain severity versus pain location in the MOBILIZE Boston study: chronic pain and lower extremity function. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 64(7); 763-770.

Espinoza I, Osorio P, Torrejon MJ, Lucas-Carrasco R and Bunout D (2011) Validation of the WHOQOL-BREF quality of life questionnaire among Chilean older people. Revista medica de Chile 139(5); 579-586.

Evan GI and Vousden KH (2001) Proliferation, cell cycle and apoptosis in cancer. Nature 411(6835); 342-348.

Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL et al. (2011) Definition and classification of cancer cachexia: an international consensus. The lancet oncology 12(5); 489-495.

Ferlay J, Shin H, Bray F, Forman D, Mathers C and Parkin D (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. International journal of cancer 127(12); 2893-2917.

Ferreira KASL, Kimura M, Teixeira MJ, Mendoza TR, da Nobrega JCM, Graziani SR et al. (2008) Impact of cancer-related symptom synergisms on health-related quality of life and performance status. Journal of pain and symptom management 35(6); 604-616.

Franck LS, Greenberg CS and Stevens B (2000) Pain assessment in infants and children. Pediatric Clinics of North America 47(3); 487-512.

Freitas SL, Arantes SL and de Barros SMO (1998) Role of the obstetric nurse in the Anhanguera community, Campo Grande (MS), for the prevention of cervical uterine cancer. Revista Latino-Americana de Emfermagem 6(2); 57-64.

Ganzeboom HBG and Treiman DJ (2010) Occupational status measures for the new International Standard Classification of Occupations ISCO-08; with a discussion of the new classification. Annual Conference of International Social Survey Programme, Lisbon.

111

Garra G, Singer AJ, Taira BR, Chohan J, Cardoz H, Chisena E et al. (2010) Validation of the Wong-Baker FACES pain rating scale in pediatric emergency department patients. Academic Emergency Medicine 17(1); 50-54.

Garrouste-Orgeas M, Troche G, Azouley E, Caubel A, de Lassence A, Cheval C et al. (2004) Body mass index. Intensive care medicine 30(3); 437-443.

Gholami A, Araghi MT, Shamsabadi F, Bayat M, Dabirkhani F, Moradpour F et al. (2016) Application of the World Health Organization Quality of Life Instrument, Short Form (WHOQOL-BREF) to patients with cataract. Epidemiology and health 38.

Giardini A, Giorgi I, Sguazzin C, Callegari S, Ferrari P, Preti P et al. (2011) Knowledge and expectations of patients in palliative care: issues regarding communication with people affected by life- threatening diseases. G Ital Med Lav Ergon 33(1 Suppl A); A41-46.

Ginieri-Coccossis M, Triantafillou E, Tomaras V, Liappas IA, Christodoulou GN and Papadimitriou GN (2009) Quality of life in mentally ill, physically ill and healthy individuals: The validation of the Greek version of the World Health Organization Quality of Life (WHOQOL-100) questionnaire. Annals of general psychiatry 8(1); 23.

Greco MT, Roberto A, Corli O, Deandrea S, Bandieri E, Cavuto S et al. (2014). Quality of cancer pain management: an update of a systematic review of undertreatment of patients with cancer. Journal of Clinical Oncology 32(36); 4149-4154.

Grosset AB, Roberts MS, Woodson ME, Shi M, Swanton RE, Reder RF et al. (2005) Comparative efficacy of oral extended-release hydromorphone and immediate- release hydromorphone in patients with persistent moderate to sever pain: two randomized controlled trials. Journal of pain and symptom management 29(6); 584-594.

Gunn GB, Mendoza TR, Fuller CD, Gning I, Frank SJ and Beadle BM (2013) High symptom burden prior to radiation therapy for head and neck cancer: a patient- reported outcomes study. Head & neck 35(10); 1490-1498.

Gupta GP and Massagué J (2006) Cancer metastasis: building a framework. Cell 127(4); 679-695.

Guy-Coichard C, Nguyen DT, Delorme T and Boureau F (2008) Pain in hereditary neuromuscular disorders and myasthenia gravis: a national survey of frequency, characteristic, and impact. Journal of pain and symptom management 35(1); 40- 50.

Hadi S, Zhang L, Hird A, Sa E, and Chow E (2008) Validation of symptom clusters in patients with metastatic bone pain. Current Oncology 15(5); 211.

Halvorsrud L, Kalfoss M and Diseth A (2008) Reliability and validity of the Norwegian WHOQOL-OLD module. Scandinavian journal of caring sciences. 22(2); 292- 305.

112

Harding R, Powell RA, Kiyange F, Downing J and Mwangi-Powell F (2010) Provision of pain-and symptom-relieving drugs for HIV/AIDS in sub-Saharan Africa. Journal of pain and symptom management 40(3); 405-415.

Hermann PC. Huber SL, Herrler T, Aicher A, Ellwart JW, Guba M et al. (2007) Distinct populations of cancer stem cells determine tumor growth and metastic activity in human pancreatic cancer. Cell stem cell 1(3); 313-323.

Hickey WS (2010) Meditation as medicine: A Critique. CrossCurrents 60(2); 168-184.

Hicks CL, von Baeyer CL, Spafford PA, van Korlaar I and Goodenough B (2001) The Faces Pain Scale-Revised: toward a common metric in pediatric pain measurement. Pain 93(2); 173-183.

Hognas E, Kauppila A, Hinkula M, Tapanainen JS and Pukkala E (2016) Incidence of cancer among grand multiparous women in Finland with special focus on non- gynaecological cancers: A population-based cohort study. Acta Oncologica 53(3); 370-376.

Hollard JC (2002) History of psycho-oncology: overcoming attitudinal and conceptual barriers. Psychosomatic medicine 64(2); 206-221.

Hollingsworth A, Ruhm CJ and Simon K (2017) Macroeconomic conditions and opioid abuse. Journal of health economics 56, 222-233.

Holtan A and Kongsgaard UE (2009) The use of pain descriptors in cancer patients. Journal of pain and symptom management 38(2); 208-215.

Hoskins W, Pollard H, Daff C, Odell A, Garbutt P, McHardy A et al. (2010) Low back pain in junior Australian Rules football: a cross-sectional surver of elite juniors, non-elite juniors and non-football playing controls. BMC musculoskeletal disorders 11(1); 241.

Jackson EL, Willis N, Mercer K, Bronson RT, Crowley D, Montoya R et al. (2001) Analysis of lung tumor inintiation and progression using conditional expression of oncogenic K-ras. Genes & development 15(24); 3243-3248.

Jaracz K, Kalfoss M, Gorna K and Baczyk G (2006) Quality of life in Polish respondents: psychometric properties of the Polish WHOQOL-Bref. Scandinavian Journal of Caring Science 20(3); 251-260.

Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T et al. (2008) Cancer statistics. CA: a cancer journal for clinicians 58(2); 71-96.

Jensen TS, Baron R, Haanpää M, Kalso E, Loeser JD, Rice ASC et al. (2011) A new definition of neuropathic pain. Pain 152(10); 2204-2205.

Jonsson T, Christrup LL, Hojsted J, Villesen HH, Albjerg TH, Ravn-Nielsen LV et al. (2011) Symptoms and side effects in chronic non cancer pain: patient report vs systemic assessment. Acta anaesthesiologica Scandinavica 55(1); 69-74.

113

Jung B and Reidenberg MM (2007) Physicians being deceived. Pain Medicine 8(5); 433-437.

Kaasa T and Wessel J (2001) The Edmonton Functional Assessment Tool: further development and validation for use in palliative care. Journal of Palliative Care 17(1); 5.

Kapstad H, Rokne B and Stavem K (2010) Psychometric properties of the Brief Pain Inventory among patients with osteoarthritis undergoing total hip replacement surgery. Health and quality of life outcomes 8(1); 148.

Katsaros N and Anagnostopoulou A (2002) Rhodium and its compounds as potential agents in cancer treatment. Critical reviews in oncology/ hematology 42(3); 297-308.

Kelsey JL, Gammon MD and John EM (1993) Reproductive factors and breast cancer. Epidemiologic reviews. 15(1); 36.

Khan N, Afaq F and Mukhtar H (2008) Cancer chemoprevention through dietary antioxidants: progress and promise. Antioxidants & redox signalling 10(3); 475- 510.

Kim EB, Han HS, Chung JH, Park BR, Lim SN, Yim KH et al. (2012) The effectiveness of a self-reporting bedside pain assessment tool for oncology inpatients. Journal of palliative medicine 15(11); 1222-1233.

Klepstad P, Loge JH, Borchgrevink PC, Mendoza TR, Cleeland CS and Kaasa S (2002) The Norwegian Brief Pain Inventory questionnaire: translation and validation in cancer pain patients. Journal of pain and symptom management 24(5); 517-525.

Knotkova H and Pappagallo M (2007) Adjuvant analgesics. Anesthesiology clinics 25(4); 775-786.

Krageloh CU, Henning M, Hawken SJ, Zhao Y, Shepherd D and Billington R (2011) Validation of the WHOQOL-BREF quality of life questionnaire for use with medical students. Education for Health 24(2); 545.

Kumar SP (2011) Utilization of Brief Pain Inventory as an assessment tool for pain in patients with cancer: a focused review. Indian journal of palliative care 17(2); 108.

Kvale R, Auvinen A, Adami HO, Klint A, Hernes E, Moller B et al. (2007) Interpreting trends in prostate cancer incidence and mortality in the five Nordic countries. Journal of the National Cancer Institute 99(24); 1881-1887.

La Vecchia C, Negri E, Gentile A and Franceschi S (1992) Family history and the risk of stomach and colorectal cancer. Cancer 70(1); 50-55.

Las Hayas C, Arroyabe EL and Calvete E (2015) Resilience in family caregivers of persons with acquired brain injury. Rehabilitation psychology 60(3); 295.

114

Leila, NM, Pirkko H, Eeva P, Eija K and Reino P (2006) Training medical students to manage a chronic pain patient: both knowledge and communication skills are needed. European Journal of Pain 10(2); 167.

Lekka D, Pachi A, Tselebis A, Zafeiropoulos G, Bratis D, Evmolpidi A et al. (2014) Pain and anxiety versus sense of family support in lung cancer patients. Pain research and treatment 2014; 3129.

Liang SY, Chen KP, Tsay SL, Wu SF, Chuang YH, Wang TJ et al. (2013) Relationship between belief about analgesics, analgesic adherence and pain experience in taiwanese cancer outpatients. Asian Pacific Journal of Cancer Prevention 14(2); 713-716.

Lopez J and Tait SWG (2015) Mitochondrial apoptosis: killing cancer using the enemy within. British journal of cancer 112(6); 957-962.

Lowery AJ, Kell MR, Glynn RW, Kerin MJ, Sweeney KJ (2012) Locoregional recurrence after breast cancer surgery: a systematic review by receptor phenotype. Breast cancer research and treatment 133(3); 831-841.

Lussier D, Huskey AG and Portenoy RK (2004) Adjuvant analgesics in cancer pain management. The oncologist 9(5); 571-591.

Ly HG, Dupont P, Geeraerts B, Bormans G, Van Laere K, Tack J et al. (2013) Lack of endogenous opioid release during sustained visceral pain: a [11C] carfentanil PET study. PAIN 154(10); 2072-2077.

Mansilla S, Garcia-Ferrer I, Mendez C, Salas JA, Portugal J (2010) Differential inhibition of restriction enzyme cleavage by chromophore-modified analogues of the antitumour antibiotics mithramycin and chromomycin reveals structure- activity. Biochemical pharmacology 79(10); 1418-1427.

Marinangeli F, Ciccozzi A, Leonardis M, Aloisio L, Mazzei A, Paladini A et al. (2004) Use of strong opioids in advanced cancer pain: a randomized trial. Journal of pain and symptom management 27(5); 409-416.

Mayer LD and Janoff AS (2007) Optimizing combination chemotherapy by controlling drug ratios. Molecular Interventions 7(4); 216.

Mazaheri M (2010) Overall, and specific life satisfaction domains: preliminary Iranian students norms. Iranian journal of public health 39(2); 89.

McCaffery M (1968) Nursing practice theories related to cognition, bodily pain, and man-environment interactions. University of California Print. Office.

McCaffery M, Ferrell BR and Pasero C (2000) Nurses‘ personal opinions about patients‘ pain and their effect on recorded assessments and titration of opioid doses. Pain Management Nursing 1(3); 79-87.

McCoy D, Kembhavi G, Patel J and Luintel A (2009) The Bill & Melinda Gates Foundation‘s grant-making programme for global health. The Lancet 373(9675); 1645-1653.

115

McDonald DD, LaPorta M and Meadows-Oliver M (2007) Nurses‘ response to pain communication from patients: a post-test experimental study. International journal of nursing studies 44(1); 29-35.

McGregor MJ, Fadhil W, Wharton R, Yanagisawa Y, Presz M, Pritchard A et al. (2015) Aberrant P53 expression lacks prognostic or predictive significance in colorectal cancer: results from the VICTOR trial. Anticancer research 35(3); 1641-1645.

Merskey H (1994) Logic, truth and language in concepts of pain. Quality of Life Research 3(1); S69-S76.

Million Women Study Collaborators (2003) Breast cancer and hormone-replacement therapy in the Million Women Study. The Lancet 362(9382); 419-427.

Mishra SI, Scherer RW, Snyder C, Geigle PM, Berlanstein DR and Topaloglu O (2012) Exercise interventions on health-related quality of life for people with cancer during active treatment. Clinical Otolaryngology 37(5); 390-392.

Modan B, Hartge P, Hirsh-Yechezkel G, Cherit A, Lubin F, Beller U et al. (2001) Parity, Oral Contraceptives, and the Risk of Ovarian Cancer among Carriers and Noncarriers of a BRCA1 or BRCA2 Mutation. New England Journal of Medicine 345(4); 235-240.

Navolanic PM and McCubrey J (2005) Pharmacological breast cancer therapy. International journal of oncology 27(5); 1341-1344.

Nedjat S, Montazeri A, Holakouie K, Mohammad K and Majdzadeh R (2008) Psychometric properties of the Iranian interview-administered version of the World Health Organization's Quality of Life Questionnaire (WHOQOL-BREF): a population-based study. BMC Health Services Research 8(1); 61.

Nord C, Bjoro T, Ellingsen D, Mykletun A, Dahl O, Klepp O et al. (2003) Gonadal hormones in long-term survivors 10 years after treatment for unilateral testicular cancer. European urology 44(3); 322-328.

Nowak MA, Komarova NL, Sengupta A, Jallepalli PV, Shih IM Vogelstein B et al. (2002) The role of chromosal instability in tumour initiation. Proceedings of the National Academy of Sciences 99(25); 16226-16231.

Ogboli-Nwasor EO, Makama JG and Yusufu LMD (2013) Evaluation of knowledge of cancer pain management among medical practitioners in a low-resource setting. Journal of pain research 6; 71-77.

Ohaeri JU, Awadalla AW, El-Abassi AHM and Jacob A (2007) Confirmatory factor analytical study of the WHOQOL-Bref: experience with Sudanese general population and psychiatric samples. BMC Medical Research Methodology 7(1); 37.

Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET et al. (1982) Toxicity and response criteria of the Eastern Coorperative Ocology Group. American Journal of Clinical Oncology 5(6); 649-655. 116

Okuyama T, Wang XS, Akechi T, Mendoza TR, Hosaka T and Charles S (2004) Adequacy of cancer pain management in a Japanese Cancer Hospital. Japanese journal of clinical oncology 34(1); 37-42.

Opoku SY, Benwell M and Yarney J (2007) Knowledge, attitudes, beliefs, behaviour and breast cancer screening practices in Ghana, West Africa. Pan African Medical Journal 11(1).

Paley CA, Johnson MI, Tashani OA and Bagnall AM (2015) Acupuncture for cancer pain in adults. The Cochrane database of systemic reviews 10, CD007753.

Paredes T, Simoes MR and Canavarro MC (2010) Psychometric properties of the World Health Organization Quality of Life Questionnaire (WHOQOL-100) in Portuguese patients with sarcoma. Psychology, health & medicine 15(4); 420- 433.

Patel P, Hanson DL, Sullivan PS, Novak RM, Moorman AC, Tong TC et al. (2008) Incidence of types of cancer among HIV-infected persons compared with the general population in the United States, 1992-2003. Annals of internal medicine 148(10); 728-736.

Peters GJ, Van der Wilt CL, Van Moorsel CJA, Kroep JR, Bergman AM and Ackland SP (2000) Basis for effective combination cancer chemotherapy with antimetabolites. Pharmacology & therapeutics 87(2-3); 227-253.

Peterson RA (1994) A meta-analysis of Cronbach‘s coefficient alpha. Journal of consumer research 12(2); 381-391.

Peuckmann V, Ekholm O, Rasmussen NK, Moller S, Groenvoid M, Christiansen P et al. (2007) Health-related quality of life in long term breast cancer survivors: nationwide survey in Denmark. Breast cancer research and treatment 104(1); 39.

Pharoah PDP, Day NE, Duffy S, Easton DF and Ponder BAJ (1997) Family history and the risk of breast cancer: a systematc review and meta-analysis. International journal of cancer 71(5); 800-809.

Pommier Y (2006) Topoisomerase 1 inhibitors: camptothecins and beyond. Nature Reviews Cancer 6(10); 789.

Portenoy RK (1992) Cancer pain: pathophysiology and syndromes. The Lancet 339 (8800); 1026-1031.

Portenoy RK, Bruns D, Shoemaker B and Shoemaker SA (2010) Breakthrough pain in community-dwelling patients with cancer pain and non cancer pain, part 2: impact on function, mood, and quality of Life. Journal of opioid management 6(2); 97-108.

Prommer EE (2015) Pharmacological management of cancer-related pain. Cancer Control 22(4); 412-425.

117

Puri S, Ashat M, Pandey A, Goel NK, Singh A and Kaushal V (2014) Socio- demographic characteristics of cancer patients: Hospital based cancer registry in a tertiary care hospital of India. Indian journal of cancer 51(1);1.

Renehan AG, Tyson M, Egger M, Heller RF and Zwahlen M (2008) Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. The Lancet 371(9612); 569-578.

Reyes-Gibby CC, Duc NB, Yen NP, Nga NH, Tran TV, Guo H et al. (2006) Status of cancer pain in Hanoi, Vietnam: A hospital-wide survey in a tertiary cancer treatment center. Journal of pain and symptom management 31(5); 431-439.

Roth RS, Geisser ME, Theisen-Goodvich M and Dixon PJ (2005) Cognitive complaints are associated with depression, fatigue, female sex, and pain catastrophizing in patients with chronic pain. Archives of physical medicine and rehabilitation 86(6); 1147-1154.

Russell SJ, Federspiel MJ, Peng KW, Tong C, Dingli D, Morice WG et al. (2014) Remission of disseminated cancer after systemic oncolytic virotherapy. Mayo Clinic proceedings 89(7); 926-933.

Rustoen T, Geerling JI, Pappa T, Rundstrom C, Weisse I, Williams SC et al. (2013) How nurses assess breakthrough cancer pain, and the impact of this pain on patients‘daily lives-Results of a European survey. European Journal of Oncology Nursing 17(4); 402-407.

Ryan CJ, Smith MR, De Bono JS, Molina A, Logothetis CJ, De Souza P et al. (2013) Abiraterone in metastatic prostate cancer without previous chemotherapy. New England Journal of Medicine 368(2); 138-148.

Siegel EM, Salemi JL, Villa LL, Ferenczy A, Franco EL and Giuliano AR (2010) Dietary consumption of antioxidant nutrients and risk of incident cervical intraepithelial neoplasia. Gynecologic oncology 118(3); 289-294.

Singh AS and Masuku MB (2014) Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of Economics, Commerce and Management 2(11); 1-22.

Skevington SM, Lotfy M and O‘Connell KA (2004) The World Health Organization‘s WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group. Quality of life Research 13(2); 299-310.

TesfayeM, Olsen MF, Medhin G, Friis H, Hanlon C and Holm L (2016) Adaptation and validation of the short version WHOQOL-HIV in Ethiopia. International journal of mental health systems 10(1); 29.

Tessaro L, Bandieri E, Costa G, Fornasier G, Iorno V, Pizza C et al. (2010) Use of oxycodone controlled-release immediately after NSAIDs: a new approach to obtain good pain control. European Review for Medical and Pharmacological Sciences 14(2); 113-121.

118

Tewari KS, Sill MW, Long HJ III, Penson RT, Huang H, Ramondetta LM et al. (2014) Improved survival with bevacizumab in advanced cervical cancer. New England Journal of Medicine 370(8); 734-743.

Tomera K, Gleason D, Gittelman M, Moseley W, Zinner N, Murdoch M et al. (2001) The gonadotropin-releasing hormone antagonist abarelix depot versus luteinizing hormone releasing hormone agonist leuprolide or goserelin: initial results of endocrinological studies. The Journal of urology 165(5); 1585-1589.

Upadhyay C, Cameron K, Murphy L and Battistella M (2014) Measuring pain in patients undergoing hemodialysis: review of pain assessment tools. Clinical kidney journal 7(4); 367-372.

Vargas-Schaffer G (2010) Is the WHO analgesic ladder still valid? Twenty-four years of experience. Canadian Family Physician 56(6); 514-517.

Von der Maase H, Hansen SW, Roberts JT, Ricci S, Dogliotti L, Oliver T et al. (2000). Gemcitabine and cisplatin versus methotrexate, vinblastine, doxorubicine and cisplatine in advanced or metastatic bladder cancer: results of a large, randomized , multinational. Journal of clinical oncology 18(17); 3068-3077.

Wallace M, Rauck RL, Moulin D, Thipphawong J, Khanna S and Tudor IC (2008) Conversion from standard opioid therapy to once-daily oral extended-release hydromorphone in patients with chronic cancer pain. Journal of International Medicine Research 36(2); 343-352.

Wang XS, Cleeland CS, Mendoza TR, Yun YH, Wang Y, Okuyama T et al. (2010) Impact of cultural and linguistic factors on symptom reporting by patients with cancer. Journal of the National Cancer Institute 102(10); 732-738.

Wang XS, Rhines LD, Shiu AS, Yang JN, Selek U, Gning I et al. (2012) Stereotactic body radiation therapy for management of spinal metastases in patients without spinal cord compression: a phase 1-2 trial. The lancet oncology 13(4); 395-402.

Weiner LM, Surana R and Wang S (2010) Monoclonal antibodies: versatile platforms for cancer immunotherapy. Nature Reviews Immunology 10(5); 317.

Weston A and Harris CC (2003) Multistage carcinogenesis. Holland-Frei Cancer Medicine. 6th edition. Hamilton (ON): BC Decker.

Wilkie DJ, Molokie R, Boyd-Seal D, Suarez ML, Kim YO, Zong S et al. (2010) Patient-reported outcomes: descriptors of nociceptive and neuropathic pain and barriers to effective pain management in adult outpatients with sickle cell disease. Journal of the National Medical Association 102(1); 18.

Williams LA, Agarwal S, Bodurka DC, Saleeba AK, Sun CC and Cleeland CS (2013) Capturing the patient's experience: using qualitative methods to develop a measure of patient-reported symptom burden: an example from ovarian cancer. Journal of pain and symptom management 46(6); 837-845.

Wiredu EK and Armah HB (2006) Cancer mortality patterns in Ghana: a 10-year review of autopsies and hospital mortality. BMC public health 6(1); 159. 119

World Health Organization (2015) Cancer Pain Relief 2nd edition. Geneva: World Health Organization.

Yu S, Shen W, Yu L, Hou Y, Han J and Richards HM (2014) Safety and efficacy of once-daily hydromorphone extended-release versus twice daily oxycodone hydrochloride controlled-release in Chinese patients with cancer pain; a phase 3 randomized, double-blind, multicenter study. The Journal of Pain 15(8); 835- 844.

Zeitoun AA, Dimassi HI, Chami BA and Chamoun NR (2013) Acute pain management and assessment: are guidelines being implemented in developing countries (Lebanon). Journal of evaluation in clinical practice 19(5); 833-839.

120

APPENDIX Appendix 1: Approval letter from Ethics Committee

121

Appendix 2: Certificate of registration from Research and Development Unit, Komfo Anokye

Teaching Hospital

122

Appendix 3: Notification letter from the study site

123

Appendix 4: A completed CHRPE application form

124

125

126

127

128

129

130

131

Appendix 5: Patient information leaflet and consent form Patient Information Leaflet and Consent Form

This leaflet must be given to all prospective patients to enable them know enough about the research before deciding to or not to participate Title of Research: Cancer pain: assessment, management and effect on quality of life of outpatients at the Oncology Directorate, Komfo Anokye Teaching Hospital Names and affiliations of researchers: This study is being conducted by Mrs Akua Afriyie Abruquah, a pharmacology student of the Faculty of Pharmacy and Pharmaceutical Sciences, Kwame Nkrumah University of Science and Technology (KNUST), in collaboration with Prof. Eric Woode: professor in pharmacology and lecturer at the Faculty of Pharmacy and Pharmaceutical Sciences, KNUST, Dr. Ernest Bawuah Osei-Bonsu, Head of Department, Oncology Directorate, Komfo Anokye Teaching Hospital (KATH) and Pharmacist Kofi Boamah Mensah; oncology pharmacist, Oncology Directorate, KATH, Kumasi, Ghana.

Background: Cancer has become a public health concern globally due to the socioeconomic impact it exerts on patients and their carers. Cancer is commonly associated with pain. In view of this, this study is being proposed to determine the treatment approaches to the management of cancer pain in Ghana; specifically (KATH). Purpose of research: The purpose of this study is to assess cancer pain; the various medicines prescribed for patients with cancer pain and evaluate the effects of pain on the quality of life of patients.

Procedure of the research, what shall be required of each patient and approximate total number of patients that would be involved in the research: As a participant in this study, you would be required to provide some personal details and answer some questions pertaining to their condition from the questionnaires provided. Information already existing in your medical folder would also be documented later for the study.

Risk(s) involved in the study: Answering the questions may require you to spend more time at the hospital than you usually do. We would appreciate the time that would be spent in providing investigators with the required information for the study.

Benefit(s) involved in the study: Results from the study would help improve upon how your cancer pain is being managed.

Confidentiality: All information gathered during the study would be coded and be limited to only investigators. Your identity (name, phone number or address) would help investigators make follow up for clarification if need be. Efforts have been put

132

in place to ensure that any information obtained during the study is used solely for research purposes and confidentiality is assured. However, no name or identity would be used in the analysis or publication of results. All documents containing identities would be handed to the research and ethics department of the hospital after the study is completed.

Voluntariness: Your participation in this study is entirely voluntary.

Refusal to participate: In case you refuse to participate in the study, your decision would not affect how your condition is currently being managed at the hospital.

Withdrawal from the research: You may choose to withdraw your participation at any time during the research and no explanation would be required. You may also choose not to answer any question or reveal any information you find uncomfortable or private.

Consequence of Withdrawal: There will be no consequence, loss of benefit or care to you if you choose to withdraw from the study. Please note however, that some of the information that may have been obtained from you without identifiers (name etc.), before you chose to withdraw, may have been modified or used in analysis reports and publications. These cannot be removed anymore. We do promise to make good faith effort to comply with your wishes as much as practicable.

Contacts: If you have any enquiries concerning this study, please do not hesitate to contact Mrs Akua Afriyie Abruquah on the number 0201797954.

Further, if you have any concern about the conduct of this study, your welfare or your rights as a research participant, you may contact:

The Office of the Chairman Committee on Human Research and Publication Ethics Kumasi Tel: 03220 63248 or 020 5453785

133

CONSENT FORM

Statement of person obtaining informed consent: I have fully explained this research to ______and have given sufficient information about the study, including that on procedures, risks and benefits, to enable the prospective participant make an informed decision to or not to participate.

DATE: ______NAME: ______

Statement of person giving consent: I have read the information on this study/research or have had it translated into a language I understand. I have also talked it over with the interviewer to my satisfaction.

I understand that my participation is voluntary (not compulsory).

I know enough about the purpose, methods, risks and benefits of the research study to decide that I want to take part in it.

I understand that I may freely stop being part of this study at any time without having to explain myself.

I have received a copy of this information leaflet and consent form to keep for myself.

NAME: ______

DATE: ______SIGNATURE/THUMB PRINT: ______

Statement of person witnessing consent (Process for Non-Literate Patients):

I (Name of Witness) certify that information given to (Name of Patient), in the local language, is a true reflection of what l have read from the study Patient Information Leaflet, attached.

WITNESS‘ SIGNATURE (maintain if patient is non-literate): ______

134

Appendix 6: Research protocol RESEARCH PROTOCOL Study background Cancer has become a public health concern globally due to the socioeconomic impact it exerts on patients and their carers [1]. Cancer is a disease characterized by loss in the normal control mechanisms that govern cell survival, proliferation and differentiation [2]. The incidence and geographic distribution of cancer are related to multiple factors including sex, age, race, genetic predisposition and exposure to environmental carcinogens. The World Health Organization (WHO) has proposed a method for cancer pain relief consisting of a three-step treatment protocol ranging from the use of non-opioid analgesics to weak and then strong opioids according to need [3]. It is recommended that adjuvant analgesics be added to each step if need be [4]. Poorly controlled cancer pain is a significant public health problem throughout the world [5]. There are many barriers that lead to the under treatment of cancer pain. One important barrier has been identified to be inadequate measurement and assessment of cancer pain [6]. In view of this, this study is being proposed to determine the treatment approaches to the management of cancer pain in Ghana; specifically the Komfo Anokye Teaching Hospital (KATH). The outcome of this study will form the basis for the development of evidence based national guidelines for the management of cancer pain in Ghana.

Study Aim The aim of this study is to determine the pharmacological approaches to the management of cancer pain at KATH, Kumasi, Ghana. Study Objectives 1. To determine the types of cancers presented at the KATH. 2. To determine the epidemiology of cancer as presented at KATH. 3. To determine the prescribing pattern of cancer medications. 4. To assess cancer pain in patients. 5. To determine the pharmacotherapy of pain in cancer patients at KATH. 6. To assess how cancer pain can impart the quality of life of patients.

Study Hypothesis or Conceptual framework Pharmacological agents reduce cancer pain.

Study Design The study will be based on both the use of secondary data obtained from patients‘ medical folders and a descriptive cross-sectional design. Patients‘ medical folders will be reviewed upon patient‘s consent after which a pain assessment questionnaire (the Brief Pain Inventory) and a quality of life assessment questionnaire (the World Health Organization Quality of Life- Bref) will be administered to patients. Sample size determination Using the Yamane‘s formula and bearing in mind the total number of patients who reported to the Oncology Directorate, KATH in 2014, starting from the medical folder of the first consenting patient, the medical records of all other consenting patients will be selected and reviewed until a total of about 210 medical folders are realized.

135

Data collection tools Epi-info will be used to design a questionnaire to extract demographic and clinical information such as the patients‘ age, gender, length of hospital stay, type of cancer, comorbidities, presence or absence of metastasis and the types of cancer treatment modalities prescribed from the patient folders. The Brief Pain Inventory (BPI), a pre-existing pain assessment questionnaire will be used to assess the pain levels of patients using structured interviews. The World Health Organization Quality of Life- Brief version (WHOQoL-Bref) will be used to assess the quality of life of patients using structured interviews. Data handling and analysis The data captured will be entered into Microsoft Excel 2013 and transferred unto Statistical Package for Social Sciences (SPSS) Version 24.0 for windows and analyzed. Relevant tables and figures will be created from the data to allow for easy analysis and interpretation. Various hypotheses tests such as t-test, chi squared and Analysis of variance (ANOVA) depending on the type of data obtained will be used to test the level of significance of the interventions instituted for the management of cancer pain. Using a confidence interval (CI) of 95%, statistical significance was set at p ≤ 0.05, p ≤ 0.01 and p ≤ 0.001.

Inclusion criteria Records of known cancer patients reporting at the Oncology Directorate, KATH who are 18 years or older and gave informed consent will be reviewed.

Exclusion criteria Records of cancer patients who are less than 18 years will be excluded from the study. Cancer patients who gave informed consent but whose medical records are incomplete or missing will not be reviewed.

136

References

1. Zeliadt, S.B. and S.D. Ramsey, Cost–effectiveness of prostate cancer

chemoprevention among high-risk men. Expert review of pharmacoeconomics

& outcomes research, 2010. 10(5): p. 505-508.

2. Yaxley, J., R. Gardiner, and W. Yaxley, Prostate cancer - Active surveillance as

a management option. Aust Fam Physician, 2013. 42(1-2): p. 74-6.

3. Wilke, D.R., et al., Sex or survival: short-term versus long-term androgen

deprivation in patients with locally advanced prostate cancer treated with

radiotherapy. Cancer, 2010. 116(8): p. 1909-17.

4. Zumsteg, Z.S., et al., A New Risk Classification System for Therapeutic

Decision Making with Intermediate-risk Prostate Cancer Patients Undergoing

Dose-escalated External-beam Radiation Therapy. Eur Urol, 2013.

5. White, A., et al., Racial/ethnic disparities in survival among men diagnosed

with prostate cancer in Texas. Cancer, 2011. 117(5): p. 1080-8.

6. van den Bergh, R.C., et al., Disease insight and treatment perception of men on

active surveillance for early prostate cancer. BJU Int, 2009. 105(3): p. 322-8.

137

Appendix 7: Questionnaire for extracting sociodemographic, clinical characteristics and drug history of patients

Folder/ Registered No: ………………………………………. INSTRUCTION: Please tick/fill in where appropriate, unless otherwise stated Patient’s Demographics 1. Age: ……….years or Date of Birth: …………………. 2. Gender/ Sex: Male [ ] Female [ ] 3. Educational Level: No formal education [ ] Primary school [ ] Secondary school [ ] College/ University [ ] 4. Marital status: Married [ ] Single [ ] Divorced/Separated [ ] Widowed [ ] 5. Religion: Christianity [ ] Islam [ ] Traditional [ ] Other [ ] If other, please specify……………………………………… 6. Ethnicity: ……………………………………………… 7. Number of children/ parity: ………………………… 8. Employment status: Unemployed [ ] Full-time employment [ ] Part- time employment [ ] Retired [ ] 9. Occupation: ………………………………. 10. Residence: ……………………… Region/ Ghana: ……………………. 11. Referred from? (Within Ghana): …………………………………………. 12. Referred from? (Outside Ghana): ………………………………………. 13. NHIS registration: Yes [ ] No [ ] 14. Gyaeneacological history (If female) Menarche: ……………… LMP: ………………………..

Patient’s vitals in 3 consecutive visits 15. Visit 1: Weight (Kg): ………………………… Visit 2: Weight (Kg): ……………………………… Visit 3: Weight (Kg): ………………………………

138

16. Visit 1: Blood Pressure (BP/ mmHg): ………………………… Visit 2: Blood Pressure (BP/ mmHg): ………………………………. Visit 3: Blood Pressure (BP/ mmHg): ……………………………….

17. Performance Status: 0 [ ] 1 [ ] 2 [ ] 3 [ ] 4 [ ] 18. Change in performance status Yes [ ] No [ ] 19. If yes, what is the new performance status: …………………………………..

20. History of contributory lifestyle factors Alcohol consumption [ ] smoking [ ] predisposing diets [ ] Others, please specify: ……………………………………………..

21. Cancer location: Breast [ ] Prostate [ ] Gynaecological [ ] Colorectal [ ] Head and neck [ ] Bone [ ] Lung [ ] Blood [ ] Lymph node [ ] Other [ ] 22. Class of tumor: ………………………………………… 23. Stage of cancer: ………………………………………… 24. Date of reporting at KATH: …………………………… 25. Presence of metastasis Yes [ ] No [ ]

26. Past Medical History (PMH) Hypertension [ ] Diabetes mellitus [ ] Peptic ulcer disease [ ] HIV/ AIDS [ ] Other medical conditions: ……………………………

27. Clinical characteristics 28. Family history of cancer Yes [ ] No [ ] 29. Signs and symptoms (On examination): Fever [ ] Cough [ ] Weight loss [ ] Lump in breast [ ] Vaginal discharge (bloody or offensive) [ ] Rectal bleeding [ ] Pain [ ] Other: …………………………. 30. Current treatments

139

No treatment [ ] Surgery [ ] Radiotherapy [ ] Chemotherapy [ ] Hormonal therapy [ ] Other [ ] If other, please specify: ……………………………………………

31. Prescribed chemotherapeutic agents ………………………………………………………………………………… …………………………………………………………………………………

32. Number of days between diagnosis and treatment: ………………………… 33. Chemotherapeutic agent used: …………………………………………………

34. Prescribed pain medications: ……………………………………………….

35. Other procedures done Radiotherapy Yes [ ] No [ ] Brachytherapy Yes [ ] No [ ]

140

Appendix 8: The Brief Pain Inventory

The Brief Pain Inventory

141 PROTOCOL # INSTITUTION PATIENT SEQUENCE # HOSPITAL CHART #

DO NOT WRITE ABOVE THIS LINE

Brief Pain Inventory

Date: ___/___/___

Name: Last First Middle Initial

Phone: ( ) Sex: Female Male

Date of Birth: ___/___/___

1) Marital Status (at present )

1. Single 3. Widowed

2. Married 4. Separated/Divorced

2) Education (Circle only the highest grade or degree completed )

Grade 0 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 M.A./M.S.

Professional degree (please specify)

3) Current occupation ( specify titles; if you are not working, tell us your previous occupation )

4) Spouse's occupation

5) Which of the following best describes your current job status?

1. Employed outside the home, full-time 2. Employed outside the home, part-time 3. Homemaker 4. Retired 5. Unemployed 6. Other

6) How long has it been since you first learned your diagnosis? months

7) Have you ever had pain due to your present disease?

1. Yes 2. No 3. Uncertain

142

8) When you first received your diagnosis, was pain one of your symptoms?

1. Yes 2. No 3. Uncertain

9) Have you had surgery in the past month? 1. Yes 2. No If YES, what kind?

10) Throughout our lives, most of us have had pain from time to time (such as minor headaches, sprains, toothaches). Have you had pain other than these everyday kinds of pain during the last week?

1. Yes 2. No

10 a) Did you take pain medications in the last 7 days?

1. Yes 2. No

10 b) I feel I have some form of pain now that requires medication each and every day. k

1. Yes 2. No

IF YOUR ANSWERS TO 10, 10a, AND 10b WERE ALL NO, PLEASE STOP HERE AND GO TO THE LAST PAGE OF THE QUESTIONNAIRE AND SIGN WHERE INDICATED ON THE BOTTOM OF THE PAGE. IF ANY OF YOUR ANSWERS TO 10, 10a, AND 10b WERE YES, PLEASE CONTINUE.

11) On the diagram, shade in the areas where you feel pain. Put an X on the area that hurts the most.

Front Back

Right Left Left Right

143

12) Please rate your pain by circling the one number that best describes your pain at its worst in the last week.

0 1 2 3 4 5 6 7 8 9 10 No Pain as bad as Pain you can imagine

13) Please rate your pain by circling the one number that best describes your pain at its least in the last week.

0 1 2 3 4 5 6 7 8 9 10 No Pain as bad as Pain you can imagine

14) Please rate your pain by circling the one number that best describes your pain on the average.

0 1 2 3 4 5 6 7 8 9 10 No Pain as bad as Pain you can imagine

15) Please rate your pain by circling the one number that tells how much pain you have right now.

0 1 2 3 4 5 6 7 8 9 10 No Pain as bad as Pain you can imagine

16) What kinds of things make your pain feel better (for example, heat, medicine, rest)?

17) What kinds of things make your pain worse (for example, walking, standing, lifting)?

18) What treatments or medications are you receiving for pain?

19) In the last week, how much relief have pain treatments or medications provided? Please circle the one percentage that most shows how much relief you have received.

0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % No Complete Relief Relief

144

20) If you take pain medication, how many hours does it take before the pain returns?

1. Pain medication doesn't help at all 5. Four hours

2. One hour 6. Five to twelve hours

3. Two hours 7. More than twelve hours

4. Three hours 8. I do not take pain medication

21) Check the appropriate answer for each item. I believe my pain is due to:

Yes No 1 . The effects of treatment (for example, medication, surgery, radiation, prosthetic device). Yes No 2 . My primary disease (meaning the disease currently being treated and evaluated). Yes No 3 . A medical condition unrelated to my primary disease (for example, arthritis ). Please describe condition:

22) For each of the following words, check Yes or No if that adjective applies to your pain.

Aching Yes No

Throbbing Yes No

Shooting Yes No

Stabbing Yes No

Gnawing Yes No

Sharp Yes No

Tender Yes No

Burning Yes No

Exhausting Yes No

Tiring Yes No

Penetrating Yes No

Nagging Yes No

Numb Yes No

Miserable Yes No

Unbearable Yes No

145

23) Circle the one number that describes how, during the past week, pain has interfered with your:

A. General Activity