Changes in Health-Related Quality of Life for Patients with Breast Cancer in Norway: An Analysis of Health-Related Quality of Life and Quality-Adjusted Life Years derived from EQ-5D-5L and 15D, and Patient Satisfaction in a Seven Month Perspective

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Changes in Health-Related Quality of Life for Patients with Breast Cancer in Norway

An Analysis of Health-Related Quality of Life and Quality-Adjusted Life Years derived from EQ-5D-5L and 15D, and Patient Satisfaction in a

Seven Month Perspective

Author: Supervisor:

Marie Grøvdal Thoresen Associate Professor

Student number: 582348 Eline Aas

Thesis Submitted as Part of the

European Master in Health Economics and Management

UNIVERSITY OF OSLO June 2017

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Changes in Health-Related Quality of Life for Patients with Breast Cancer in Norway

An Analysis of Health-Related Quality of Life and Quality-Adjusted Life Years derived from EQ-5D-5L and 15D, and Patient Satisfaction in a Seven Month Perspective

http://www.duo.uio.no/

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Declaration in lieu of oath

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Abstract

Breast cancer is the most frequent type of cancer occurring in females; the incidence and survival rates are increasing, however number of deaths has increased as well. Patients with breast cancer are confronted with symptoms of a physical nature, as well as psychosocial distress, both influencing their quality of life. New treatments become available for breast cancer patients regularly, and breast cancer is more often detected at an early stage due to the mammography screening programme. Health-related quality of life is used as a measure of outcomes and effect in economic evaluations. Despite the fact that economic evaluations give guidance to decision makers in assessment of treatments, few studies have examined the health-related quality of life among breast cancer patients in Norway.

The main objective of this thesis was to evaluate changes in health-related quality of life and quality-adjusted life years among breast cancer patients in Norway using the EQ-5D-5L and the 15D questionnaires for measurement, as well as patient satisfaction. Individual-level data from 141 female breast cancer patients in Norway has been used to analyse, describe and evaluate the impact of breast cancer diagnosis and treatment on health-related quality of life.

Correlational analyses and log-linear regression analyses have been performed in the analysis, as well as analysis of change in health-related quality of life.

The outcome for the breast cancer patients in this sample was a reduction in health-related quality of life from baseline to seven months after surgery. This reduction was larger for the 15D than the EQ-5D-5L. The difference between the estimated quality-adjusted life years based on the EQ-5D-5L and the 15D was however not as large. The findings indicate a difference in what dimensions of health are captured by the two instruments of measurement, for patients with breast cancer.

The outcomes were found to be related to the severity of disease and how comprehensive treatment the patients received. In addition, some effect was found from employment status.

15D seems to be more sensitive to change in HRQoL for breast cancer patients, compared to the EQ-5D-5L. Further research is recommended on the HRQoL of breast cancer patients.

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Acknowledgements

First of all, I would like to thank my supervisor Eline Aas who gave me the opportunity to work with this very interesting study. She has not only shared her knowledge with me and aided me through this process with valuable insights and guidance; she has also helped me keep motivated until the end.

This project was undertaken within the European Union 7th Framework Programme European Health Care Outcomes, Performance and Efficiency (EuroHOPE), Contract no.

241721.

The project has been made possible by the help and cooperation of many people. I am grateful to all of them:

Thank you to Ivar Guldvog, for being positive and supportive in conducting this study at Sykehuset Telemark, and to Rosmari and Kjersti for taking on the responsibility of recruiting and following patients, as well as collecting the questionnaires. The study would not have been possible without your contribution, and was improved thanks to Kjersti who kindly took the time to answer my questions during this process. Thank you to all the nurses who have been involved in the process of recruitment and collection of patients and data.

A thank you is in order to Sanju Sinwal, William Sørby and Lage Aas Marthinsen, for taking the time and making the effort of entering the collected data from the questionnaires.

Harri Sintonen helped with adjustment of missing values from the 15D-questionnaire, making the results in this thesis more accurate; thank you for taking on that task.

Last but not least, to all my friends and family: thank you for your support not only during the process of writing this thesis, but throughout the master’s programme. I would especially like to thank all my wonderful co-students; I am lucky to have had the pleasure of making so many new friendships these past two years.

Oslo, June 2017 Marie Grøvdal Thoresen

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List of Tables

Table 2.1 Staging by TNM-classification ...7

Table 4.1 Summary Description of the Dataset ...24

Table 4.2 Patient Characteristics at Baseline and after Seven Months ...25

Table 4.3 Sample Distribution of Stages by TNM-classification ...27

Table 4.4 Description of Surgical Treatment: Type of Surgery, Axillary Dissection and Repairing Plastic Surgery ...28

Table 4.5 Description of Combinations of Adjuvant Treatment: Overall Distribution and Distribution According to Stage ...29

Table 4.6 Length of Stay (LOS) and Time since Treatment (measured at seven months) ...30

Table 6.1 Mean, median and range of Responses to the EORTC IN-PATSAT32 scales (Linearly Transformed) ...42

Table 6.2 Health-related quality of life and Quality-Adjusted Life Years ...43

Table 6.3 HRQoL and QALYs according to Stage ...44

Table 6.4 Distribution of HRQoL interval – scores from the EQ-5D-5L vs. the 15D ...45

Table 6.5 Spearman Correlation Matrix for HRQoL-scores ...45

Table 6.6 Spearman Correlational Analyses of Dimensions in the EQ-5D-5L vs. the 15D ....46

Table 6.7 Changes in HRQoL 15D from Baseline to Seven Months ...47

Table 6.8 Changes in HRQoL EQ-5D-5L from Baseline to Seven Months ...47

Table 6.9 Change in EQ-VAS from Baseline to Seven Months ...48

Table 6.10 Log-Linear Regression Analysis with QALYs as dependent variables and demographic variables as independent variables (Hypotheses H1_A-C) ...49

Table 6.11 a) Log-Linear Regression Analysis with QALYs as dependent variables and age and stage as independent variables (Hypothesis H1_D) ...50

Table 6.11 b) Log-Linear Regression Analysis with QALYs as dependent variables and time to surgery and length of stay as independent variables (Hypothesis H1_D) ...50

Table 6.11 c) Log-Linear Regression Analysis with QALYs as dependent variables and Adjuvant Treatment as independent variables (Hypothesis H1E) ...50

Table 6.11 d) Log-Linear Regression Analysis with QALYs as dependent variables and age and treatment as independent variables (Hypothesis H1E) ...51

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Table 6.11 e) Log-Linear Regression Analysis with QALYs as dependent variables and type of surgery, axillary dissection and number of hospitalisations as independent variables (Hypothesis H1_E) ...51 Table 6.12 Log-Linear Regression Analysis with QALYs as dependent variables and the EORTC IN-PATSAT32 scales as independent variables (Hypothesis H3) ...52 Table A.1 Age distribution in the Breast Cancer Population versus the Sample ...75 Table A.2 Frequency Distribution of EQ-VAS Scores at Baseline and after

Seven Months...75 Table A.3 Frequency Distribution of Responses to the EQ-5D-5L at Baseline ...76 Table A.4 Frequency Distribution of Responses to the 15D at Baseline and after

Seven months ...77 Table A.5 Distribution of Responses to the EORTC IN-PATSAT32...78 Table A.6 Correlational Analyses EORTC IN PATSAT-32 and 15D, EQ-5D-5L

and EQ-VAS ...79

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List of Figures

Figure 2.1 Description of the Treatment Path for Breast Cancer Patients from

the Norwegian Directorate of Health ... 10 Figure 3.1 Estimation of QALYs by the area-under-the-curve method (example) ... 18 Figure 4.1 Age Distribution in the Breast Cancer Population of Norway vs. the Sample ... 26 Figure 6.1 Frequency Distribution of EQ-VAS Scores at Baseline and after

Seven Months... 37 Figure 6.2 a) Frequency Distribution of Responses to the EQ-5D-5L at Baseline ... 38 Figure 6.2 b) Frequency Distribution of Responses to the EQ-5D-5L after

Seven Months... 38 Figure 6.3 a) Frequency Distribution of Responses to the 15D at Baseline ... 40 Figure 6.3 b) Frequency Distribution of Responses to the 15D after Seven Months ... 41

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List of Abbreviations

CUA Cost-Utility Analysis

DCE Discrete Choice Experiment

EORTC The European Organisation for Research and Treatment of Cancer EuroHOPE European Health Care Outcomes, Performance and Efficiency GLM Generalized Linear Model

HRQoL Health-Related Quality of Life HSU Health State Utility

LOS Length of Stay

MAUI Multi-Attribute Utility Instrument MAUT Multi-Attribute Utility Theory MID Minimally Important Difference PROM Patient Reported Outcome Measure QALY Quality-Adjusted Life Year

QoL Quality of Life

SG Standard Gamble

TTO Time Trade-Off

VAS Visual Analogue Scale

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1 Introduction

Breast cancer is the most common type of cancer among females in Norway. Both the

incidence rate and the survival rate for breast cancer are increasing. Even though the survival rate has improved, the number of deaths due to breast cancer has increased over the last fifty years, both in Norway and worldwide (Cancer Registry of Norway, 2016, Naume et al., 2016, Stewart and Wild, 2014).

The treatment for breast cancer is under constant development, and new medications and treatment regimens become available continuously. In breast cancer treatment, better

prerequisites for endocrine treatment through development of new drugs, as well as increased knowledge about the effects of oestrogen in hormone sensitive breast cancer, are examples of new improved treatment options (Naume et al., 2016, Naume, 2015d).

Due to an increase in the breast cancer incidence, followed by the development of new treatments for breast cancer, a need for evaluation of treatment arises. The quality-adjusted life year (QALY) measure, derived from health-related quality of life (HRQoL) scores, is broadly utilised in economic evaluation of health care interventions for informing decision makers. Even though economic evaluation gives guidance to decision makers for the

assessment of new treatment, few studies of health-related quality of life among breast cancer patients in Norway have been identified; one study examined the impact of mammography screening on HRQoL, another study investigated the HRQoL among early stage breast cancer patients compared to the general population in Norway (Brazier et al., 2017, Hafslund et al., 2012, Schou et al., 2005).

1.1 Relevance

Breast cancer patients frequently experience anxiety, depression, stress, uncertainty and decreased physical and social functioning. In early stages of breast cancer, the results of both surgical treatment and oncological (medical) treatment may give sudden alterations in the patients’ physical and psychosocial well-being, as well as their quality of life (Salonen et al., 2011).

The Cancer Registry of Norway has recently initiated a pilot study where the goal is to measure quality of life (patient reported outcome measures) in prostate cancer patients at the time of diagnosis, one year and three years after diagnosis. The purpose of this study is to

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enable the assessment of actual quality of life (QoL) measures in order to improve the QoL among patients with prostate cancer. The questionnaires used include the EQ-5D-5L, the EORTC QLQ-C30 (a cancer specific questionnaire with direct questions relating to QoL) and the EPIC-26 (a prostate cancer specific questionnaire) as confirmed by Ylva Marie Gjelsvik, consultant at the Cancer Registry of Norway (E-mail correspondence, 28 April 2017)

(Kreftregisteret, 2017). The Norwegian Minister of Health and Care Services, Bent Høie stated that “This may provide us with important new information, which can lead to an improved and more precise cancer care” (Jacobsen, 2017, Kreftregisteret, 2017). The launch of such a study, demonstrates that research on health-related quality of life to a greater extent is being put into focus within the Norwegian health care system.

The National Clinical Registry for Breast Cancer of Norway does not include patient reported outcome measures (PROMs) as of yet. A workgroup has been put together that has started reviewing which instruments to use in the collection of data on PROMs among breast cancer patients in Norway. An important aim of the pilot study for patients with prostate cancer, is to create a system for collection of data on PROMs on a national level so that this may be used for other types of cancer as well (Østby et al., 2016).

HRQoL data are of importance to clinicians treating the patients, but is in addition necessary for health economic evaluations in order to aid decisions on the allocation of limited

resources. An economic evaluation is performed in order to assist the decision makers in coming to conclusions that are both cost-effective and equitable (Brazier et al., 2017).

Both the Norwegian Directorate of Health and the Norwegian Medicines Agency recommend the use of QALYs as an expression for good life-years in prioritisation. An important reason for this is the advantages of such analyses, where the QALY permits comparisons across different diseases, includes life-years and HRQoL in the same measure and is based on preferences for health states (The Norwegian Medicines Agency, 2012, The Norwegian Directorate of Health, 2012). The available resources for health care will always be exceeded by the possibilities provided by the constant development in medical technology. This is because of challenges following changes in the demographics, the population morbidity and the rising expectations from patients (The Norwegian Ministry of Health and Care Services, 2016). Since breast cancer is the most common cancer among females in Norway (and worldwide), it is of interest to examine the HRQoL of this patient group (Naume et al., 2016, WHO, 2017).

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1.2 Rationale

A utility analysis estimating and evaluating HRQoL and QALYs among Norwegian breast cancer patients will be conducted using data collected in the period 2013-2016. The data to be used in this study has been collected as part of the EuroHOPE (European Health Care

Outcomes, Performance and Efficiency) project, at the Telemark Hospital in Norway. The purpose of the EuroHOPE project was comparing treatment, costs, mortality and quality for several diseases, among them breast cancer. The goal of the EuroHOPE study where the data on HRQoL among breast cancer patients has been collected, is to measure health-related quality of life and patient satisfaction among females diagnosed with and treated for breast cancer (Regional Committees for Medical and Health Research Ethics, 2011).

The objective of this thesis is to describe and evaluate the impact of breast cancer diagnosis and treatment on HRQoL, QALYs and patient satisfaction in a seven-month perspective from time of diagnosis, for patients with breast cancer in Norway. Importantly, the differences between HRQoL derived from the 15D and the EQ-5D-5L will be examined, as well as how demographics, disease characteristics, treatment and patient satisfaction, measured by the EORTC IN-PATSAT32, impact the HRQoL. This thesis will not examine the effect of one specific intervention, nor a specific therapeutic effect, but rather the outcome of HRQoL and what factors determine the change in HRQoL from time of diagnosis to seven months after diagnosis.

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1.3 Research Question

This leads me to the following research problem:

How does the health-related quality of life change for patients diagnosed with breast cancer in Norway over the first seven months following diagnosis?

Sub-questions of this research problem are the following:

Which characteristics have an impact on the health-related quality of life for Norwegian breast cancer patients when it comes to demographical characteristics, severity of disease and extent of treatment – and how?

To what extent are the results disparate when utilising the 15D versus the EQ-5D-5L for measuring the health-related quality of life among breast cancer patients?

In what manner do patient satisfaction and the health-related quality of life correspond for breast cancer patients?

1.4 Outline

In this thesis, chapter 2 first gives a brief introduction to the epidemiology, survival and prognosis, as well as causes and symptoms, and diagnostics and treatment of breast cancer. In chapter 3, methods for measuring health and health-related quality of life are presented.

Relevant research is presented at the end of this chapter. Chapter 4 familiarises the reader with the data used in this analysis, while chapter 5 presents the statistical analysis. In chapter 6, the results of the analyses are presented in tables and graphs, before chapter 7 provides a discussion of the main findings, the HRQoL-instruments, methodology, generalizability, policy implications and further research. In chapter 8 the conclusion of the thesis is presented.

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2 Breast Cancer

2.1 Epidemiology, Survival and Prognosis of Breast Cancer

Breast cancer is the most frequent type of cancer occurring in females both in Norway and worldwide; it constitutes of 22 % of all cancer cases among females in Norway (Cancer Registry of Norway, 2016, Stewart and Wild, 2014). In 1970, 1,235 females were diagnosed with breast cancer, compared to the 3,415 new cases in 2015 (Naume et al., 2016, Cancer Registry of Norway, 2016, Normannvik, 2017). The age-standardised incidence rate of breast cancer in Norway has increased from 115.8 per 100,000 person-years in 2006, to 128 per 100,000 person-years in 2015. Breast cancer primarily occurs in females above the age of 50.

However, the increasing incidence appears to include mainly females above the age of 60.

Only 5.1 % of the new cases occurred in females below the age of 40 in the years 2009-2013.

The cumulative risk of developing breast cancer at the age of 75 is 8.5 % At the end of 2015, the prevalence of breast cancer was 44,182 in Norway (Cancer Registry of Norway, 2016, Naume, 2015b).

The most common cause of death due to cancer in females worldwide is breast cancer. In Norway 663 females died of breast cancer in 2014. Breast cancer is the most important cause of life-years lost in females up to the age of 65. The estimated five-year relative survival rate has increased for all stages of disease in the period 1974-2013 and changed from 88 % to 89 % for females with breast cancer in the periods 2006-2011 to 2011-2015. In the period 2011-2015, the relative five-year relative survival rate was 100 % for breast cancer patients with stage I. For stage II, stage III and stage IV, the five-year relative survival rates, in the same time period, were 92 %, 76 % and 25 %, respectively. Though the breast cancer mortality started declining from the mid-1990s, there is a constant decline in cumulative survival among breast cancer patients, indicating a steady excess mortality (Cancer Registry of Norway, 2016, Naume et al., 2016, Stewart and Wild, 2014).

The prognosis of breast cancer is highly dependent on stage at time of diagnosis, which again is reflected in whether the breast cancer is detected early or not. The life expectancy after diagnostics and treatment has improved through decades. This is related to, among other factors, the screening programme with mammography for females aged 50-69, which has made it possible to discover the disease at an early stage and thus improve treatment

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outcomes (Naume, 2015d). The Norwegian mammography screening programme shows a 43 % reduction in breast cancer mortality among those participating in the programme (Naume et al., 2016).

2.2 Causes and Symptoms of Breast Cancer

The risk of developing breast cancer is dependent upon a range of factors that can be divided into hereditary and non-hereditary factors. Hereditary factors known today include mutations in the genes BRCA 1 and 2, causing 3-5 % of the breast cancer cases. Non-hereditary factors include an early debut of menstruation, no or late child birth, obesity, late menopause,

hormonal treatment during menopause for relief of symptoms and the consumption of alcohol (Wist et al., 2016). Risk factors such as age, family history and density of mammography are also of relevance (Stewart and Wild, 2014).

The majority of the breast cancer patients do not experience symptoms, but can however be confronted with symptoms of a physical nature, as well as psychosocial distress, both

influencing their quality of life. Fatigue and lymphedema are both problems related to breast cancer treatment mentioned in the literature (Naume, 2015g, Perry et al., 2007).

2.3 Diagnostics

Breast cancer is characterised by highly differing patterns, from rapidly growing tumours with early distant metastasis to more moderately growing tumours that do not metastasise (Naume, 2015b). The underlying principle of diagnostic examination, when there is reason to suspect breast cancer, is the triple assessment. This includes a clinical examination of the breast(s), mammography and/or ultrasound of the breast(s) and a needle biopsy (Wist et al., 2016).

The TNM-classification system describes the spread of cancer disease at time of diagnosis.

The “T” describes the size of the local tumour in and around the breast, where “T1” is the smallest. The “N” expresses whether there is spreading to the lymph nodes and the extent of lymph node spreading, while the “M” indicates the presence of distant metastases (M1 indicates there are metastases, M0 that metastases have not been located) (Naume, 2015e).

The division of T, N and M into stages I – IV and primary operable/inoperable is demonstrated in Table 2.1.

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Primary operable cancer

Stage I T1N0M0

Stage II T0-2N1M0

T2N0M0

Primary inoperable cancer

Stage II T3N0M0

Stage III T0-2N2M0

T3N1-2M0

T4N0-2M0

T0-4N3M0

Stage IV T1-4N0-3M1

(Naume et al., 2016)

According to the Cancer Registry of Norway (2016), 41.3 % of breast cancer patients had stage I, 33.9 % stage II, 11.7 % stage III and 3.3 % stage IV, while 9.8 % had unknown stages, at diagnosis. The classification and staging of breast cancer based on the TNM-system is decisive for what type of treatment the patients receive, as well as prognosis for the

patients (Naume, 2015e).

2.4 Treatment and Side-effects

There is a clear division between treatment of local disease of the breast, regional lymph node metastasis, and established spreading of the disease. When the breast cancer is local

(confined within the breast), the goal is always curative treatment. With metastatic (distant) breast cancer, there is at present time no curative treatment available (Naume, 2015a). The treatment regimen is chosen on the basis of preoperative examinations, age and functional status, the presence of comorbidities and conversation with the patient (Wist et al., 2016).

The main groups of treatment are surgery, neoadjuvant (preoperative) medical treatment and adjuvant (secondary) medical treatment with chemotherapy, endocrine treatment, treatment with antibodies and bisphosphonate (Zometa) in order to impede bone damage, as well as radiation therapy (Cancer Research UK, Felleskatalogen, 2016, Aftimos et al., 2013). The primary treatment consists either of surgery or neoadjuvant treatment with chemotherapy and radiation therapy. Adjuvant treatment is recommended to 90 % of all patients undergoing

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surgery for breast cancer, with chemotherapy and/or endocrine treatment. In addition, many patients receive radiation therapy (Wist et al., 2016).

Surgical interventions applied in breast cancer patients include diagnostic biopsy, resection with or without oncoplastic surgery (reconstructive surgery of the breast), mastectomy

(removal of the entire mammary gland) with or without primary reconstruction, sentinel node biopsy and, if indicated, axillary lymph node dissection (Naume, 2015a, Rancati et al., 2013).

Sentinel node biopsy is mainly performed in patients having early stages of cancer, during surgery, in order to determine whether there is spreading to the lymph nodes (Schlichting, 2015). The most common complications after surgery include occurrence of bleeding that demands reoperation, infection and the accumulation of exudate (Wist et al., 2016).

Medical treatment of breast cancer consists of a range of hormonal (endocrine) treatment principles, as well as a variety of effective chemotherapy regimens and immunotherapy (trastuzumab) to slow down the growth of cancer cells (Naume, 2015c, Clare, 2013). Patients having locally advanced breast cancer or who temporarily cannot undergo surgery, may receive neoadjuvant chemotherapy in order to reduce the size of the tumour, permit a less comprehensive surgery, accomplish better outcomes cosmetically and decrease the risk of postoperative complications like lymphedema (Sikov, 2017, Wist et al., 2016). Following chemotherapy patients may incur bone marrow suppression with febrile neutropenia demanding treatment, or neuropathy (Wist et al., 2016). Patients may also experience side- effects such as mucositis, diarrhoea (which is associated with a decrease in the QoL), constipation, and impairment of cognitive functioning, sexual dysfunction (altered body image and sexuality), hair loss and nausea (Aftimos et al., 2013, Clare, 2013). When treated with trastuzumab (or Herceptin), heart failure is mentioned as the most common complication (Wist et al., 2016).

When oestrogen- and progesterone receptors are present in the tumour, this indicates a probability of effect from endocrine treatment. The main drug groups for usage in adjuvant treatment include anti-oestrogen (tamoxifen) and aromatase inhibitors (Naume et al., 2016).

Hormonal treatment for breast cancer in females depend on age and whether she is pre- or post-menopausal, and is prescribed to patients with advanced and early stages of disease (Østby et al., 2016, Aftimos et al., 2013). Tamoxifen increases the chances of thrombosis and embolisms as a complication (Wist et al., 2016). Patients may, among other side-effects, experience hot flashes, eye problems, musculoskeletal pain, bone loss, cardiovascular events and cognitive dysfunction from endocrine treatment (Aftimos et al., 2013).

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Radiation therapy is administered postoperatively, as primary treatment or as palliative treatment. For patients having undergone a mastectomy, radiation therapy is recommended where there is uncertain evidence of a cancer cell free margin of resection. Radiation therapy can be given before surgery, if the tumour is too large and the effect of chemotherapy is not satisfactory, or if surgery is difficult to conduct (Naume, 2015f). In conjunction with radiation therapy, skin reactions, scarring, swelling and pigmentation changes may occur (Wist et al., 2016).

The Norwegian Ministry of Health and Care Services has defined a standardised path of treatment for all patients where a reasonable suspicion of breast cancer is present. The treatment path starts with a referral from the patients’ physician to a specialist ward for examination. The examination is to take place within a week from referral. A diagnosis is to be set one week after finalisation of the examination. Following diagnosis, treatment is to commence within ten to thirteen days, depending on whether the primary treatment is surgical or medical (neoadjuvant treatment). After six months to one year, the patient comes in for control (Wist et al., 2016).

The treatment path presented in figure 2.1 corresponds to the treatment path for breast cancer patients set by the Ministry of Health and Care Services. This treatment path has been

followed by the patients of this study – as confirmed by Kjersti Tinholt Kristensen (Oncology Nurse) at Telemark Hospital (E-mail correspondence, 24 March 2017).

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Figure 2.1 Description of the Treatment Path for Breast Cancer Patients from the Norwegian Directorate of Health (Wist et al., 2016)

Referral: To ward of examination.

Examination:

Maximum 7 days from referral.

Clinical, mammography and/or ultrasound.

Diagnosis:

TNM-staging.

Maximum a week after start of examination.

Neo-adjuvant treatment: Medical treatment prior to surgery.

Medical

treatment: Within 10 days of final diagnostics.

Surgery: A maximum of 13 days after diagnosis.

Answer biopsy:

Within three weeks from surgery.

Adjuvant treatment

Control: Six months to one year after end of treatment.

Maximum time from referral to start medical treatment:

24 days.

Maximum time from referral to surgery:

27 days.

6 months

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3 Measuring Health

The World Health Organization defines health as “a state of complete physical, mental and social well-being, and not merely the absence of disease and infirmity” (World Health Organization, 1948). An essential part of health is that it incorporates individual experience of factors like pain and emotions, which are difficult to measure externally. The measurement of health can be viewed as measuring the overall functioning of parts and processes of

people’s bodies and minds (Hausman, 2015).

The notion that health is a substantial element of general well-being, while at the same time acknowledging that other components than health affects the overall quality of life, is combined in HRQoL-measures. When measuring HRQoL, individual patients are asked to rate their health in a subjective manner. Different definitions of HRQoL exist, and there is some disagreement regarding which definition is more appropriate. Not everyone recognises the inclusion of “social well-being” when it comes to defining health (Brazier et al., 2017).

Two definitions of HRQoL, both including social aspects, are:

“Health-related quality of life is the value assigned to duration of life as modified by the impairments, functional states, perceptions, and social opportunities that are influenced by disease, injury, treatment, or policy” (Feeny et al., 2017, p. 168).

“A person’s subjective perception of the impact of health status, including disease and treatment, on physical, psychological and social functioning and well-being” (Brazier et al., 2017, p. 182).

3.1 The Measurement of Health-Related Quality of Life

Measurement of HRQoL can be performed by the use of preference-based or non-preference- based approaches. Non-preference-based measures are founded in social science and clinical disciplines, and many non-preference-based measures of health outcomes have been

developed outside the health economics field. The non-preference-based measures do not take into account the relative importance of the health dimensions. On the other hand, preference-based measures are based on economics and decision science, and include such importance weighting of the dimensions describing health. The preference-based

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measurements can be performed directly and indirectly. Direct measurement techniques include the standard gamble (SG), time trade-off (TTO), discrete choice experiment (DCE) and the visual analogue scale (VAS). The multi-attribute approach is an indirect measurement technique, in which the respondents complete questionnaires, before the responses are scored by existing preference weights. In preference-based measures the scoring focuses on the valuation of health states. Different values might be generated from the different techniques (Brazier et al., 2017, Feeny et al., 2017, Lamu et al., 2016).

In the direct method SG, respondents are presented with a choice between a definite intermediate outcome, and the uncertainty of a gamble containing two different outcomes.

One is better and one is worse than the definite intermediate outcome. The two alternatives presented to the respondent can for instance include:

1. An intervention with two possible outcomes: living with normal health for t years, or dying immediately after intervention – each with

corresponding probabilities (p and 1 – p, respectively).

2. A certain outcome of a chronic health state for t years.

The probability p is varied until the respondent is indifferent with respect to alternative 1 and 2 (and is no longer able to choose) (Brazier et al., 2017).

The TTO technique is meant to be a simpler alternative to the SG method. Here the respondents have the choice between two certain outcomes, for example:

1. Living t years in a health state worse than perfect health.

2. Living in full health for x years, where x is lower than t.

Number of years x is then varied until the respondent is indifferent as to which alternative is chosen (Brazier et al., 2017).

The DCE differs from the TTO in that a sequence of questions is not asked in order to identify a point of indifference. Instead, the DCE entails asking the respondent to make a choice between two or more alternatives. The alternatives are illustrated by levels along various dimensions (for instance health status based on the EQ-5D). In the valuation of health, the respondents give an indication of their stated choice from a series of options.

DCEs can contribute to achieving an improved understanding of patient experience and their trade-offs between various health outcomes and treatments (Brazier et al., 2017, Drummond et al., 2015, Feeny et al., 2017).

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Lastly, the VAS entails the measurement of health by a line having clearly defined end-points and interval properties. The respondents can rate their health state by indicating where on the scale they would place their own health. The VAS represents the most practicable of the techniques used to value health states, with high rates of response and completion. Though it is not strictly preference-based, it is easy to administer and understand, and thus to complete, for the respondents. Some concern has however been expressed when it comes to the

reliability of this method for evaluating an individual’s own health (Brazier et al., 2017).

Multi-attribute utility measurement, include instruments with multiple dimensions (attributes) of health status (Feeny et al., 2017). Two different categories of multi-attribute utility

instruments (MAUIs) for measuring HRQoL include disease-specific measures and generic preference-based measures (Lamu et al., 2016, Brazier et al., 2017).

The disease-specific measures focus on the impacts on HRQoL from a specific disease, and could be appropriate in evaluating the efficacy of treatment. In some conditions, it has been demonstrated that generic preference-based measures of health are unsuitable. For instance, the treatment regimens in cancer patients depend upon how severe the disease is; because of this, instruments that can discriminate between levels of disease are important. Disease- specific instruments have the ability to capture minimal changes in particular health states.

There is however concern that disease-specific instruments will increase differences of health state utility values, in return having an effect on results in economic evaluation. Disease- specific measures might not capture comorbidities to the same degree as generic instruments do, as they are limited to comparing health interventions within the specific disease.

Furthermore, the disease-specific instruments are not fitted for comparison between different diseases, which in turn reduces comparability (Brazier et al., 2017, Drummond et al., 2015, Teckle et al., 2011). Examples of disease-specific measures include the lung cancer-specific questionnaire FACT-L and the cancer-specific EORTC QLQ-C30 questionnaire, which further may be supplemented for instance by the breast cancer-specific QLQ-BR32. Disease- specific measures exist for many different diseases and conditions (Brazier et al., 2017, EORTC Quality of Life).

A variety of generic preference-based measures of health are available. These have a

description of health status consisting of questions and response categories, and a value-set to be attached to every health state described, for the conversion of utility into a zero (death) to one (full health) scale. Some of the most frequently used generic preference-based measures include the EQ-5D, the 15D, the SF-6D, the Quality of Well-Being scale (QWB), the Health

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Utilities Index (HUI) and the AQoL-8D (the assessment of quality of life). Generic

preference-based measures focus on a wide array of dimensions of HRQoL, which may be influenced by any condition or disease, but the various instruments differ largely in how health is described (which dimensions and levels are included) and in which methods are used to value health. Regardless, it is argued that using these measures enable the comparison between different diseases and treatments (Brazier et al., 2017, Richardson et al., 2015a, Teckle et al., 2011).

The proposed relation between economic evaluations of health care interventions utilising different instruments for measuring HRQoL, is weakened by the fact that various instruments give disparate utilities for the same respondents (Gamst-Klaussen et al., 2015, Richardson et al., 2015a). A measure’s empirical validity is formed by the descriptive system and the valuation methods utilised. There are considerable debates regarding the most suitable techniques of valuation. The source of values (preferences), whether from a patient

population or the general public, is based on a normative understanding (Brazier et al., 2017).

Instrument reliability is investigated by assessing how coherent and reproducible the outcomes from applying a specific measure are. For instance, in individuals who have a steady health, the compliance between scores measured at two different time-points, should be high (Feeny et al., 2017).

According to the guidelines for economic evaluation of health care interventions from The Norwegian Directorate of Health (2012), the recommended instruments for measurement of HRQoL include generic MAUIs. However, no recommendation is made regarding exactly what instrument is preferred (The Norwegian Directorate of Health, 2012).

In the present sample of breast cancer patients, the generic preference-based instruments EQ- 5D-5L and 15D have been used for measuring HRQoL – both instruments seek assessment of the same construct: utility (Richardson et al., 2015b, Drummond et al., 2015). The 15D was found to be used in 6.9 % of 1663 studies using MAUIs in the period 2005 to 2010, while the EQ-5D questionnaire was the MAUI utilised most frequently (63 %). There is however no gold standard for which instrument to use at present (Richardson et al., 2015b). The EQ-5D is, despite this, the recommended instrument by the UK National Institute of Health and Excellence (Richardson et al., 2014). While the 15D has adopted a “within the skin”

approach to description of health, focusing on malfunctions and limitations of the body, the EQ-5D has based the description of health on handicap, i.e. problems influencing life in a social context. The 15D has one dimension related to handicap (Richardson et al., 2014).

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3.2 EQ-5D-5L

The EQ-5D-5L consists of five dimensions with five levels each, where level 1 is the highest and level 5 is the lowest score. The EQ-5D-5L includes the same dimensions as the EQ-5D- 3L, but with small alterations as well as the inclusion of two new levels. The three-level version defines 243 health states, whereas in the five-level version 3,125 health states are generated in total (Brazier et al., 2017, Drummond et al., 2015).

Devlin et al. (2016) have developed a value set for EQ-5D-5L for England, based on a hybrid model that combines the data from TTO tasks and DCE tasks (Devlin et al., 2016). The preferences of the English public indicate that pain/discomfort and anxiety/depression are the health problems of largest significance, at the same time problems with self-care and usual activities are of less significance (Devlin et al., 2016). Calculation of the values for a health state is performed by summing the values for the level of each dimension (excluding those scored as 1), before multiplying this by the weighted average of the slope, which is a fixed coefficient (0.9675) and subtract this from the constant (1.000). The minimum value (level 5 on all dimensions; health state 55555) is -0.281, and the proportion of health states rated as worse than dead (below 0) is substantially lower than in previous EQ-5D (e.g. the three-level version and the crosswalk) value sets (Devlin et al., 2016). A temporary tariff was estimated by mapping the EQ-5D-5L onto the EQ-5D-3L so that the EQ-5D-5L could be used before the finalisation of the value set, which will be relevant for those who want compatibility with previous studies using the three-level version (Brazier et al., 2017).

The EQ-5D-5L was developed because of an unease related to insensitivity of the EQ-5D-3L, and increasing evidence of ceiling effects in the three-level version. Initial findings indicate that the fraction of respondents describing their health as having “no problems” (level 1 on all dimensions; 11111) has been somewhat reduced in the five-level version, i.e. the ceiling effect seems to have been reduced in the new five-level version compared to the three-level version (Brazier et al., 2017).

The EQ-VAS is considered a part of the EQ-5D-5L, hence the EQ-5D-5L is a two-part instrument. Only the EQ-5D-5L questionnaire can be utilised for calculation of HRQoL and thus QALYs (EuroQol). The scores of the EQ-VAS are anchored at 100 and 0 corresponding to the “best imaginable health” and the “worst imaginable health”, respectively, and is

represented by the point on the line divided by 100 (Glick et al., 2015). It has been questioned

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whether the formulations of the anchors may give more room for variation in interpretation and responses (Brazier et al., 2017).

3.3 15D

The 15D questionnaire consists of 15 dimensions, each having 5 ordinal levels. This enables the 15D to define billions of health states. Level 1 indicates normal function while level 5 indicates severe problems/difficulties (15d-instrument.net, Sintonen, 2001). The

questionnaire is self-completed by respondents (Brazier et al., 2017). A zero-to-one scale where 0 corresponds to being dead, 0.0162 corresponds to being unconscious or comatose and 1 corresponds to “full” HRQoL (no problems on any of the dimensions), represents the overall HRQoL. The 15D conceptually acknowledges the World Health Organization

definition of health, and includes physical, mental and social well-being (15d-instrument.net, Sintonen, 2001).

The valuation of the 15D was performed by using a variant of a VAS in which respondents from the Finnish general population were asked to view the scale as including ratio properties (a rating scale). MAUT was utilised to estimate an additive function (Brazier et al., 2017, Richardson et al., 2014). Few get a perfect score (1.0) on the 15D, indicating that there are no major ceiling effects, but few score below 0.4 as well (Richardson et al., 2014).

The values of health states are calculated by the use of an additive formula. A value is accredited to every dimension level, multiplied by a weight that represents the related significance of that dimension and summed to acquire a single index (score) (Brazier et al., 2017, 15d-instrument.net). If three or more dimensions are missing, a 15D-score cannot be calculated (15d-instrument.net, Sintonen, 1994).

3.4 Quality-Adjusted Life Years (QALYs)

The optimal measure of health effect in the evaluation of benefit in economic evaluations is a generic measure of health gain including both change in quantity and quality of life, while at the same time being based on the application of health state-preferences – one such measure is the QALY (Drummond et al., 2015). Health interventions, like treatment for breast cancer, can influence the health status of an individual both in a positive or a negative direction, as well as influencing the length of life. QALYs are the sum of the number of years (expected to

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be) lived, each year being weighted by a HRQoL-weight ranging from 0 to 1 where 0 corresponds to being dead and 1 is equivalent to perfect health (Meltzer et al., 2017). It is a measure of improvement in health (Weinstein et al., 2009). The QALY-measure can be viewed as a measure of health, because it is calculated by combining survival probabilities and HRQoL-measures, which are defined by health status. Thinking of QALYs as a measure of utility is based on the maximisation of welfare, which can be viewed as a more common objective for making decisions than the maximisation of health; this is due to the fact that health is not the only source of welfare (Meltzer et al., 2017).

A welfarist-based cost-utility analysis (CUA) builds the theory of the QALY upon utility theory. The QALY is a representation of health as a social aim; the focus is on producing social goods. Here, the number of QALYs depicts the level of utility for an individual regarding their own health. The health status remains constant, thus there is just one health state between the present and death, referring to a chronic health state. For this to be

applicable, the conditions “mutual utility independence between quantity and quality of life”,

“constant proportional time trade-off” and “risk neutrality with respect to life years” must be fulfilled. These conditions are founded on the expected utility theory (choice under

uncertainty). If there is more than one ”temporary” health state followed by death, and if the amount of QALYs related to the complete health profile over time is to be equivalent to the sum of individual temporary health states, “additive separability” must be fulfilled. This entails the understanding that the utility of a health state is not affected by preceding or following states, but is unaffected by other states – which seems unlikely (Brazier et al., 2017).

The CUA, for which QALYs are used as a measure of health benefits, is mainly considered to be built on a non-welfarist approach. Here the measure of social welfare is built on extrinsic evaluations of the well-being of afflicted patients. The descriptions of the health-states are from the patients, while the valuation is usually derived from a representative sample of the population (Brazier et al., 2017, Owens et al., 2017).

A method for calculation of QALYs is the area-under-the-curve method. Here an assumption is made that the health status development between the measurement times is steady over time; this way the utility scores may be approximated by an aligned curve (Brazier et al., 2017).

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Figure 3.1 Estimation of QALYs by the area-under-the-curve method (example)

Looking at Figure 3.1, the area-under-the-curve method entails calculating the area beneath the HRQoL-curve for the different time intervals and adding them. If the HRQoL would have been perfect (1.0) throughout the one year, the QALY for one year would similarly be 1.0 (Feeny et al., 2017).

3.5 Patient Satisfaction measured by the EORTC IN-PATSAT32

Patient satisfaction may be characterised as a subjective understanding of health care due to the individual perception of services and emotional reactions formed by them. The target of measurement is the patient’s contact with the health care services, what effect this has on the patient outcomes, and to what degree the patient experiences having received excellent medical care. Studies have concluded that patients who are not satisfied are less likely to return for additional care, as well as being more unlikely to adhere to medication regimes. A connection between patient satisfaction and HRQoL has been implied, though there is no clear direction of causality. Some indication points toward a relationship between lower levels of satisfaction and poorer health outcomes (Lloyd, 2014).

The EORTC IN-PATSAT32 is a patient satisfaction questionnaire composed of 32 items for evaluation of cancer patients’ judgment of the quality of care in hospitals from nurses, doctors and other personnel. The response scale for rating allows the patients to reply on a scale of 1 to 5, where 1 is “poor”, 2 is “fair”, 3 is “good”, 4 is “very good” and 5 is

“excellent” on each of the items (Brédart et al., 2005, EORTC Quality of Life). The 32 items

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Baseline 7 months 1 year

HRQoL

Time

HRQoL

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of the EORTC IN-PATSAT32 are divided into fourteen multi-item and single-item scales. A linear transformation of the responses is performed, transforming the responses into scales of 0-100, where the higher score indicates a higher satisfaction-level (Brédart et al., 2005).

3.6 Research on HRQoL among Breast Cancer Patients and Patient Satisfaction

In general, there are different determinants of health which together affects an individuals’

health. A higher income and social status is associated with better health. A lower level of education is associated with poorer health, a higher level of stress and a lower level of self- confidence. Social support networks are important with respect to health, and a higher degree of support from family and friends is associated with better health. People who are in

employment have been found to be healthier than those who are unemployed (World Health Organization, 2017b).

3.6.1 HRQoL among Breast Cancer Patients

The main burden on HRQoL for patients with breast cancer includes the fear of recurrence and/or death, lymphedema, fatigue, early menopause and problems with returning to work (Pinheiro et al., 2017). Both physical and psychosocial factors are affected as a result of being diagnosed with breast cancer. Anxiety, depression, stress and uncertainty are some factors that influence a breast cancer patients’ quality of life. Younger females have a higher probability of experiencing emotional distress – older women seem to be experiencing a better physical and emotional well-being than the younger women. The majority of changes in the QoL among breast cancer patients have been found to occur between the first and the second year after surgery for breast cancer (Salonen et al., 2011, Salonen et al., 2009, Badger et al., 2004, Engel et al., 2004).

Salonen et al. (2011) examined changes in HRQoL during the first six months from surgery, using the breast cancer specific EORTC QLQ-BR23 questionnaire. They found that a smaller disruption of QoL was associated with breast conserving surgery compared to a total

mastectomy – particularly when looking at body image and sexual functioning. Axillary surgery causing problems with the functioning of the arm also decreased the QoL. Females receiving chemotherapy or hormonal therapy had a larger risk of decrease in body image scores. Employed females had a higher risk of enduring negative alterations in their body

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image than retired females. Females who received chemotherapy had a higher risk of poorer sexual functioning compared to the females who did not receive chemotherapy (Salonen et al., 2011).

Schou et al. (2005) examined the HRQoL among 161 Norwegian early stage breast cancer patients compared to the general population, using the EORTC QLQ-C30. The breast cancer patients had significantly more problems with depression, anxiety and worries, irritations, difficulties remembering and a harder time concentrating than the general population at baseline. Older age was found to be significantly correlated with better role and emotional functioning. A higher level of education was linked to better emotional functioning at

baseline and one year after surgery. Marital status and employment status were not correlated with global health QoL or functioning. The mildest stages of breast cancer were associated with a better global health QoL and role functioning after three and twelve months. Cancer spread to the lymph nodes was correlated with a poorer emotional functioning three months after surgery. Breast conserving surgery was associated with a better physical functioning than mastectomy after three months and one year. Poorer social functioning was found in patients receiving chemotherapy treatment, while hormonal and radiation therapies were not associated with global health QoL. 28 % of the breast cancer patients in this study had a negative effect on their sexual life due to their disease and treatment. The patients with breast cancer reported to experience significantly more problems with sleep quality, appetite loss and diarrhoea than the general population. After twelve months however, the patients reported similar or less symptoms than the general population (Schou et al., 2005).

Another study using the EORTC QLQ-C30 performed in Germany, investigated the QoL in primary breast cancer patients prospectively. They found that the global QoL improved from before surgery until end of adjuvant treatment. 47 % of the patients reported a good QoL at both measurements, while 28 % had an improvement and 10 % had a decrease in QoL. 15 % stayed in a bad health state from first to last measurement. Patients who underwent

chemotherapy treatment were more frequently found to have a consistently poor QoL or a decrease in QoL. An indication of impact from radiation therapy and disease progression was not found (Wöckel et al., 2017).

When estimating minimally important differences in HRQoL-measures, Alanne et al. (2015) measured a mean 15D-score at baseline of 0.921 for patients with breast cancer, where 99.6 % were females out of 906 patients in total, and the mean age was 59 years. After six

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months, this patient group had a mean change in 15D-score of -0.025 from baseline (Alanne et al., 2015).

Luutonen et al. (2014) compared HRQoL for Finnish breast cancer patients to the general Finnish population, measured by the 15D. The mean age of the patients was 58 years, and mean time from first breast cancer diagnosis was seven months. The objective of this study was to increase the understanding of the situation for breast cancer patients’ towards the end of adjuvant treatment. The mean 15D-score for the breast cancer patients was found to be 0.883, compared to 0.932 in the general population seven months from diagnosis. The patients had a worse score than the general population when it came to the dimensions sleeping, usual activities, mental function, depression, distress, vitality and sexual activity.

No difference was found in the 15D-score between patients aged >53 and <53 years.

Depressive symptoms were negatively associated with HRQoL; mastectomy was positively associated with HRQoL compared to breast-conserving surgery in the younger patients, but not among the older patients. Breast cancer patients undergoing adjuvant radiotherapy treatment, had an impairment of HRQoL (Luutonen et al., 2014).

Färkkilä et al. (2014) investigated the HRQoL among breast (prostate and colorectal) cancer patients with end-stage disease, using the 15D, the EQ-5D-3L and the EQ-VAS. For breast cancer patients, they found a mean HRQoL of 0.718 and 0.447 based on the 15D and the EQ- 5D-3L respectively. For the EQ-VAS, the patients had a mean score of 50 (0.5). This was based on the responses of 27 breast cancer patients, with a mean age of 66 years, and a mean time since diagnosis of 6.6 years (Färkkilä et al., 2014).

When investigating the differences in HRQoL-outcomes for the EORTC QLQ-C30, the EORTC QLQ-BR32, the EQ-5D-5L and the EQ-VAS, Wallwiener et al. (2016) found that there were significant differences in the outcomes, concluding that the chosen questionnaire influences the description of the HRQoL in breast cancer patients.

In a comparison of the EQ-5D-5L and the FACT-B (a breast-cancer specific questionnaire) among Asian breast cancer patients, Lee et al. (2013) found that the EQ-5D-5L is a

reasonable alternative or additional instrument to the FACT-B, offering at least the same precision-level in discriminative performance. The mean HRQoL-score derived from the EQ-5D-5L was estimated to 0.76. The sample included patients treated with curative and palliative purposes, 43 % received chemotherapy and/or radiation therapy (Lee et al., 2013).

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3.6.2 Patient Satisfaction

Patient satisfaction can be said to be influenced by two factors: characteristics of the patient and the health care provider. Health care is to a certain degree individualised, and the characteristics of the patient affects the degree of individualisation of treatment. Patients having a more severe disease are likely to be hospitalised longer, get more aggressive

treatment, and have a larger probability of experiencing complications from treatment. Thus, these patients will most likely have a less pleasant experience than patients with a less severe disorder. Defining what predictors affect patient satisfaction could aid in the interpretation of the scores, making it possible to adjust for these (Perneger, 2004).

In an international validation study of the EORTC IN-PATSAT32, it was found that the EORTC IN-PATSAT32 did not significantly correlate with the EORTC QLQ-C30. This indicates that the EORTC IN-PATSAT32 questionnaire measures different issues than HRQoL. None of the IN-PATSAT32 scales managed to distinguish between patient groups based on age and educational level, but the instrument scales could discriminate between patients with different expectations with respect to care. It was to a lower degree able to discriminate between patients who went through treatment related with high versus low toxicity (Brédart et al., 2005).

Brédart et al. (2007) examined determinants of patient satisfaction in the oncology setting, using the EORTC IN-PATSAT32 questionnaire. They found that patients with an educational level lower than university level reported a lower overall satisfaction. Patients treated in a medical ward with high treatment toxicity had a lower overall satisfaction, while patients with a relatively higher global health status (measured with the QLQ-C30 questionnaire) reported a (significantly) higher level of satisfaction (and vice versa) with both doctor and nurse interpersonal skills, information provision and availability, as well as general satisfaction. Age and gender was not found to be associated with patient satisfaction, the same was true for disease characteristics such as stage and time since diagnosis. The EORTC IN-PATSAT32 scales have limited variability because of a ceiling effect; many patients score the maximum (Brédart et al., 2007).

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4 Data

The data utilised in this study has been collected as part of the EuroHOPE-project “Health related quality of life (HRQoL) and patient satisfaction among women with breast cancer: A comparison between 7 European countries”. The study and collection of data has been approved by the Regional Committees for Medical and Health Research Ethics (Appendix II D) (Regional Committees for Medical and Health Research Ethics, 2011). The main

characteristics of the dataset are summarised in table 4.1.

At baseline, data was collected using the standardised HRQoL-questionnaires 15D and EQ- 5D-5L. The EORTC IN-PATSAT32 was used to collect information about patient

satisfaction related to the first hospitalisation for breast cancer treatment. Medical data was collected from the patients’ medical records prior to and following surgery (Patient

Information Part A1); including date of diagnosis, date of hospitalisation, time to surgery, length of stay, adjuvant treatment, TNM-classification and type of surgery (Appendix II B.1).

Furthermore, information on demographic data, like year of birth, gender, marital status, educational level and employment status was obtained by a self-reporting questionnaire (Patient Information Part A2), prior to treatment or surgery (Appendix II B.2). After

approximately seven months, patients filled out the standardised HRQoL-questionnaires as at baseline. Additionally, they filled out a self-report questionnaire (Patient Information Part B) regarding characteristics; including current marital status, current employment status and information regarding the treatment. Information on treatment include number of days first admission, total number of hospital days, time since first admission due to breast cancer, time since surgery, chemotherapy and radiation therapy, as well as whether surgery was first treatment (Appendix II B.3).

Patients were included in the study from September 2013 until June 2015. The sample

consists of 150 female breast cancer patients diagnosed and treated at Telemark Hospital. The data available is limited to the follow-up period of approximately seven months. The majority of the patients completed the baseline questionnaires within 10 days from diagnosis (67 %), while 15 % had filled out these questionnaires between 11 and 20 days after diagnosis and 18 % fulfilled the baseline questionnaires later than 20 days after diagnosis. All patients have received treatment for breast cancer.

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Type of data: Survey data (Questionnaires), individual-level

data.

Collection method: Questionnaires handed out at hospital and

postal survey after six months.

Research instrument:

Questionnaires:

EQ-5D-5L, EQ-VAS, 15D, EORTC IN-PATSAT32, background questionnaires including among other factors year of birth, marital status, education, employment status, TNM, date of diagnosis, provided treatment

(Appendix II B and II C).

Unit of analysis: HRQoL, non-interventional.

Target population: Female patients with breast cancer (C50 and D05).

Number of observations: 141 (150 before exclusion).

Sampling design/procedure: Data collection at baseline and after approximately six months.

Start of collection: September 2013.

End of collection:

June 2015 (last month of recruitment, follow- up of the recruited patients for the following six months approximately).

4.1 Population Cohort

Patients included in this study were female patients with newly diagnosed breast cancer – ICD-10 codes C50 and D05. C50 (breast cancer) corresponds to malignant neoplasm of the breast, including connective tissue of the breast, excluding the skin of the breast. D05 (ductal carcinoma in situ - DCIS) corresponds to carcinoma in situ (pre-invasive carcinoma) in the breast (mammae), excluding carcinoma in situ of skin of breast and melanoma in situ of the breast (skin) (Direktoratet for e-helse, 2017). Male patients with breast cancer were excluded from this analysis.

After the final collection of data from the 150 patients, one patient terminated all contact with the healthcare services, one patient’s questionnaires were incomplete, one withdrew from the study, three patients expired from the study for various reasons and three patients did not fill out the questionnaires. This leaves a total of 141 patients for further analyses. One of these patients has not filled out the 15D after seven months, and some have bypassed answering a few of the dimensions on the 15D-questionnaire and other questions.

Changes in Health-Related Quality of Life for Patients with Breast Cancer in Norway: An Analysis of Health-Related Quality of Life and Quality-Adjusted Life Years derived from EQ-5D-5L and 15D, and Patient Satisfaction in a Seven Month Perspective