UNIVERSITY OF OSLO
FACULTY OF MEDICINE
DEPARTMENT OF HEALTH MANAGEMENT AND HEALTH ECONOMICS
Breast cancer screening amongst women aged 40-69 in Norway
A cost-utility analysis estimating the minimum risk reductions needed for screening to be cost-effective
Author: (Student Number) Supervisor:
Vegard Kvilesjø: (591531) Hans Olav Melberg
Thesis submitted as a part of the of the master program European Master in Health Economics and Management.
Oslo, 06. 2021
“DECLARATION OF OATH
I hereby declare, under oath, that this master thesis has been my independent work and has not been aided with any prohibited means. I declare, to the best of my knowledge and belief, that all passages taken from published and unpublished sources or documents have been reproduced whether as original, slightly change or in thought, have been mentioned as such at the corresponding places of the thesis, by citation, where the extent of the original quotes is indicated.
The paper has not been submitted for evaluation to another examination authority, or has it been published in the form or another”
SIGNATURE
8. June, 2021. 8. June, 2021.
Hans Olav Melberg
date and signature of student date and signature of supervisor
© Vegard Kvilesjø 2021
Breast cancer screening amongst women aged 40-69 in Norway. A cost-utility analysis estimating the minimum risk reductions needed for screening to be considered cost effective
Vegard Kvilesjø http://www.duo.uio.no/
Print: Reprosentralen, Universitetet i Oslo
Abstract
Background: It has been suggested that the Norwegian National Breast Cancer Screening Program (NBCSP) should start to screen younger age groups. However, the current literature is in conflict, and there are no widely accepted estimate on both increased survival rates, and overdiagnosis as a result of screening.
Objective: What is the minimum breast cancer mortality reduction amongst women aged 40- 49 the Norwegian NBCSP needs to achieve, which makes extending the eligible participant population from age 50-69 to 40-69 considered cost-effective?
Method: A Cost-Utility Analysis (CUA) was performed, where a reallocation of breast cancer patients to earlier stages simulated the present of screening. The reallocation of breast cancer patients were continued up to the point in which the Incremental Cost Effectiveness Ratio (ICER), was lower than the willingness-to-pay (WTP) per Quality Adjusted Life Year (QALY) gained threshold of kr 750 000. The risk reduction was then recorded. Multiple uncertainty analysis were conducted to analyze the impact of overdiagnosis.
Result: With 15 % overdiagnosed patients, approximately 26.8% (relative) and 7.3%
(absolute) risk reduction was needed if screening from age 40 was to be considered cost- effective. The estimated minimum risk reductions were highly dependent on the frequency of overdiagnosed patients.
Conclusion: Based on the results, with 15% overdiagnosed patients, approximately 26.8%
(relative) and 7.3% (absolute) risk reductions were needed for screening to be considered cost-effective. However, there are great uncertainty concerning the estimate, due to overdiagnosis. Additional knowledge of markers to identify tumors aggressiveness, could both reduce the uncertainty regarding overdiagnosis and cost-effectiveness, as well as reduce the impact of the main limitation of this thesis. Further research on tumor aggressiveness, is therefore recommended.
Acknowledgment
Firstly, I am profoundly grateful to my family, friends and fellow students supporting me throughout the five last years of higher education. The last year and months presented itself with great challenges due to the COVID-19 pandemic. Luckily, encouraging and supporting words have made it possible to complete this thesis, and to that I´m grateful and proud.
Secondly, I would like to thank the University of Oslo, Erasmus University Rotterdam, Management Center Innsbruck and the University of Bologna for providing the master
program: European Master in Health Economics and Management. Even with closed facilities due to the COVID-19 pandemic, the respective universities were still able to deliver lectures and exams. I have learned a considerably lot academically, but also about myself. I truly believe the last two years have been the most interesting time of my life (so far).
Lastly, I wish to express my thanks to my supervisor Hans Olav Melberg at the Institute of Health and Society at the University of Oslo. Even though physical meeting was not an option, I hope you feel the many zoom sessions and emails still were managed in the right way. Further thank you for showing interest in this thesis, and support throughout thesis process.
Vegard Kvilesjø Spydeberg, 2021
Table of content
List of abbreviations ... V List of tables, figures and graphs ... VII
1. Introduction ... 1
1.1 Cancer as a health care challenge ... 1
1.2 Introduction of the mammogram program ... 1
1.3 The suggestion by the Norwegian Cancer Association ... 2
1.4 The debate regarding the true effect of mammographic screening of breast cancer ... 3
1.5 Aim, relevance and use of this study ... 4
1.6 Presentation of the research question ... 5
1.7 Thesis structure ... 5
2. Background and Theoretical Framework ... 6
2.1 Breast cancer ... 6
2.1.1 Types of breast cancer ... 7
2.1.2 Classification of cancer diagnosis – The TNM system ... 9
2.2 Screening theory ... 12
2.2.1 Length and Lead time bias... 13
2.2.2 Overdiagnosis ... 15
2.3 Health economic evaluation ... 16
2.3.1 Different approaches of economic evaluation ... 17
2.4 Health economic modelling ... 19
2.4.1 Model uncertainty ... 20
2.5 Existing literature on mammographic screening of women in the age interval of 40-49 ... 22
2.5.1 Preliminary search ... 22
2.5.2 Presentation of relevant articles ... 24
2.5.3 Thesis contribution ... 28
3. Method ... 30
3.1 PICO and choice of model ... 30
3.2 Establishing minimum risk reductions ... 32
3.3 Model guidelines of the Norwegian Medical Agency (NoMA) ... 33
3.4 Willingness-to-pay (WTP) per QALY gained threshold ... 34
3.5 Model parameters ... 35
3.6 Model uncertainties ... 43
3.6.1 Half-Cycle-Correction (HCC) ... 43
3.6.2 Deterministic Sensitivity Analysis (DSA) - Overdiagnosis ... 44
3.6.3 Probabilistic Sensitivity Analysis (PSA) ... 45
3.6.4 Value of Information analysis (VoI) ... 45
3.7 Internal model validation ... 46
4. Results ... 50
4.1 Estimated minimum level of effect ... 50
4.2 Deterministic Sensitivity Analysis (DSA) ... 51
4.3 Probabilistic Sensitivity Analysis (PSA) ... 52
4.3.1 Cost-Effectiveness Acceptability Curve/Frontier (CEAC/F) ... 53
4.4 Value of Information Analysis ... 54
4.4.1 Expected Value of Perfect Information (EVPI and pEVPI) ... 55
4.4.2 Expected Value of Perfect Parameter Information (EVPPI) ... 55
5. Discussion ... 57
5.1 Main findings ... 57
5.1.1 Minimum RRR/ARR and the cost-effectiveness of screening at age 40-69 ... 57
5.1.2 Impact of overdiagnosis... 58
5.2 Model weaknesses/limitations and validity of the findings ... 59
5.2.1 The reallocation of breast cancer patients between the cancer stages ... 60
5.2.2 Risk reductions as a measure on the effect of screening ... 61
5.2.3 The use of aggregated data ... 62
5.3 Model strengths ... 63
5.4 Transferability of findings ... 63
5.5 Conclusion ... 63
5.5.1 Methods, findings and limitations ... 64
5.5.2 Further research ... 65
6. References ... 66
i. Appendix ... i
List of abbreviations
AJCC: American Joint Committee on Cancer ARR: Absolute Risk Reduction
CBA: Cost-Benefit Analysis CEA: Cost-Effectiveness Analysis
CEAC: Cost-Effectiveness Acceptability Curve CEAF: Cost-Effectiveness Acceptability Frontier CE-plane: Cost-Effectiveness plane
CIS: Carcinoma In Situ
CMA: Cost-Minimization Analysis
CNBSS-1: Canadian National Breast Screening Study CUA: Cost-Utility Analysis
DCIS: Ductal Carcinoma In Situ DRG: Diagnosed Related Groups
DSA: Deterministic Sensitivity Analysis ENBS: Expected Net Benefit of Sampling
EVPI: Expected Value of Perfect Information (per individual) pEVPI: Expected Value of Perfect Information (population) EVPPI: Expected Value of Perfect Parameter Information EVSI: Expected Value of Sampling Information
HCC: Half Cycle Correction
HRQoL: Health Related Quality of Life HTA: Health Technology Assessment IBC: Inflammatory Breast Cancer
IARC: International Agency for Research on Cancer ICER: Incremental Cost-Effectiveness Ratio
IDC: Invasive Ductal Carcinoma ILC: Invasive Lobular Carcinoma KM: Kaplan Meier
LCIS: Lobular Carcinoma In Situ LY: Life Year
NBCSP: National Breast Cancer Screening Program NMB: Net Monetary Benefit
NOK: Norwegian Kroner
NoMA: Norwegian Medical Agency
NIPH: Norwegian Institute of Public Health OD: Overdiagnosed
PSA: Probabilistic Sensitivity Analysis QALY: Quality Adjusted Life Year RCT: Randomized Control Trials RRR: Relative Risk Reduction
SAVI: Sheffield Accelerated Value of Information SE: Standard Error
SSB: Statistics Norway
TNBC: Triple Negative Breast Cancer USD: US Dollar
USPTF: US Preventive Services Task Force VoI: Value of Information
WTA: Willingness-to-accept WTP: Willingness-to-pay
List of tables, figures and graphs
Tables
Table 2.1: Levels within the category T (tumor size) Table 2.2: Levels within the category N (Lymph Nodes) Table 2.3: Levels within the category M (Metastases) Table 2.4: TNM stages
Table 2.5: Preliminary search findings
Table 3.1: Model requirements by the Norwegian Medical Agency (NoMA)
Table 3.2: Age and stage specific, outside screening/no attenders diagnostic parameters Table 3.3: Age and stage specific, screen detected diagnostic probabilities
Table 3.4: Modes of detection (age 40-49) proportions Table 3.5: Conditional survival probabilities
Table 3.6: Age specific mortality probabilities Table 3.7: Cost parameters
Table 3.8: Utility (QALY) parameters
Table 3.9: Population, discount rates, WTP threshold, participations parameters Table 3.10: Diagnostic validation
Table 4.1: Estimated minimum risk reduction at WTP per QALY gained threshold at kr 750 000 and 15% overdiagnosed patients
Figures
Figure 2.1: Anatomy of the breast Figure 2.2: Illustration of screening
Figure 2.3 a: Graphical illustration of lead time bias Figure 2.3 b: Graphical illustration of length time bias
Figure 2.4: Illustration of the underlying method of economic evaluation Figure 2.5 a: Markov model
Figure 2.5 b: Decision tree model Figure 3.1: Markov transition model
Figure 3.2: Process of reallocating diagnostic probabilities
Graphs
Graph 3.1: Population validation, model compared with population projection from SSB Graph 4.1: ICER and minimum RRR with different rates of overdiagnosis
Graph 4.2: CE-plane with the lower and higher bound of the estimated interval Graph 4.3 a: Lower bound´s CEAC and CEAF
Graph 4.3 b: Upper bound´s CEAF and CEAF
Graph 4.4: Lower and upper bound´s EVPI and pEVPI
Graph 4.5: EVPPI for respective parameters groups for both bounds
1. Introduction
Chapter 1 gives the introduction to the topic of breast cancer in Norway. It introduces the current impact of breast cancer on the Norwegian society as a public health issue and a source of cost for the health care system. Additionally, the uncertainties within the topic are
presented, which in turn makes the foundation of the aim and relevance of this thesis. Further, the research question is presented and explained. Lastly, an overview of the chapters is given.
1.1 Cancer as a health care challenge
Cancer remains one of the most serve public health concerns in Norway. Not only is it a challenge for the patients and their relatives as life years (LY) and quality of life (QoL) are lost, but cancer care is also costly for the society. The total estimated cancer related, direct health care costs in 2017 was estimated to NOK 20.5 billion (Oslo Economics, 2019, p.4).
When taking a socio-economic perspective (also including indirect costs, and costs due to lost life years and health related quality of life (HRQoL), the costs were estimated to be NOK 210 billion (cost as a results of lost life years and HRQoL was estimated to be approximately NOK 150 billion). With increasing cancer prevalence and increasing costs, cancer will likely remain one of the most important public health challenges for the years to come.
Of the many types of cancer, breast cancer has received great attention through the last decades, and is one of the most frequent type of cancer in Norway. According to the Cancer Registry of Norway, breast cancer is the second most frequent type of cancer, only beaten by prostate cancer (Cancer Registry of Norway, 2020, p. 13). Especially in younger age groups (25-59), breast cancer makes up 33.4 % of all cancers (Directorate of Health, 2021, p. 11).
Breast cancer is prevalent amongst both men and women. However, breast cancer is rare amongst men, whereas the most common type of cancer amongst women (3726 new cases of breast cancer amongst women in 2019) (Cancer Registry of Norway, 2021).
1.2 Introduction of the mammogram program
The Norwegian government´s answer to address the mortality rates of breast cancer amongst women in 1998, was the introduction of the mammogram program, the National Breast Cancer Screening Program (NBCSP). It started as a pilot project and gradually extended to nationwide in 2005 (Hofvind et al., 2017, p. 15). The mammogram program is organized by the Cancer Registry of Norway and aims to increase the overall survival of breast cancer
amongst women. By detecting cancer tumors in the early stages, it could result in higher likelihood of an effective and safe treatment. The program is offered to all women in Norway within the age interval of 50-69, and based on voluntary participation, which includes a breast cancer screening every other year (Cancer Registry of Norway, 2015, p. 10 ). The Cancer Registry of Norway expects approximately 75% of the eligible participants to take part in the NBCSP, however, higher participations rates are still wanted.
After the introduction of the NBCSP, it has gained wide support. Firstly, there has been a reduction of breast cancer deaths. In 1998 the mortality rate was 37 per 100 000 and was reduced to 23 per 100 000 in 2017 (Norwegian Institute of Public Health, 2018), a relative reduction of 38%. Secondly, the cost-effectiveness of an intervention is a key factor in the decision making. An external report considering the cost-effectiveness of the Norwegian NBCSP reviewed different estimates on the cost-effectiveness. The external report found the cost-effectiveness of the NBCSP range from NOK 190 000 to 479 000 per Quality Adjusted Life Year (QALY) gained (The Research Council of Norway, 2015, p. 150), where all estimates resulted in a cost per QALY below their respective willingness-to-pay (WTP) per QALY gained threshold, i.e. the NBCSP was concluded to be cost-effective. It has therefore been raised few concerns or debate regarding the cost-effectiveness of the NBCSP.
1.3 The suggestion by the Norwegian Cancer Association
The Norwegian Cancer Association has previously suggested to extend the eligible population for participation the NBCSP. As previously mentioned, the program is currently offered to women aged 50-69, however, the Cancer Association is working towards increasing the age interval to 45-74 by including women aged 45-49 and 70-74 (The Norwegian Cancer
Association, n.d.). Their suggestion is based on the favorable results with the current included age group in the NBCSP, and the association states similar suggestions have been made by other international, professional environments. The aim is include the age groups mentioned above, thus increase overall survival of breast cancer.
1.4 The debate regarding the true effect of mammographic screening of breast cancer
There is a debate regarding the true survival benefit of a mammogram program. With the introduction of the program in 1995, it did not only have the direct effect of detecting tumors in earlier stages, but had an additionally indirect effect through increasing awareness for breast cancer. The treatments of breast cancer have improved to a level, which is now causing the debate of how much reduction in mortality can be credited the NBCSP directly. A
question arises if the currently available treatments have improved to a point of which the removal of the NBCSP would have little or no effect on the overall survival. The literature provided different indications. The external evaluation states the most reliable estimate is approximately 20-30% mortality reduction due to the NBCSP (The Research Council of Norway, 2015, p. 81). However, other factors outside screening (most likely treatments), have been estimated to produce a 28% mortality reduction, whereas 0-5% mortality reduction could be credited screening (Autier, Boniol, Koechlin, Pizot & Boniol, 2017, p. 6). Hence, with the estimated survival benefit of treatments by Autier et al. (2017), it may indicate there is a low proportion of the total mortality reduction that could be credited mammographic screening directly. Screening has also been argued to produce very low survival benefits and even being harmful to the participants, thus should be abandoned (Gøtzche, 2015, p. 344).
Furthermore, the estimated effect of screening amongst the additional age groups (mainly age 40-49), the literature also seems, to an extent, to be in conflict. Firstly, screening amongst women aged 40-49 has been estimated to potentially reduce breast cancer mortality (Duffy et al., 2020, p. 1165). However, a report from the National Center of Knowledge for Health Care Services (currently a division under the Norwegian Institute of Public Health (NIPH)), states that the relative risk reduction is very low and it is highly debatable whether it would be worth implementing it (Bjørndal & Forsetlund, 2007, p. 24). For women aged 70-74, a Swedish screening program estimated the effect of screening to be 24% and 6%, for excess mortality and breast cancer deaths respectively (Jonsson, Törnberg, Nyström & Lenner , 2003, p. 360).
Hence, there are no estimates of mortality reduction caused by screening which is widely accepted in the literature on both the current and the suggested, additional age groups.
Secondly, the debate is further fueled by overdiagnosed and overtreated breast cancer patients, an aspect explored by Paci & Duffy (2005). These are patients of which would not have been diagnosed with cancer in their lifetime without screening. Hence, the patients receive invasive cancer treatment which will affect the patient´s HRQoL, as well producing
unnecessary cost for the health care system. However, there is currently no good marker to differentiate between overdiagnosed- and lethal cancer tumors (Paci & Duffy, 2005, p. 266).
Due to overdiagnosis in screening, it has been argued that screening does more harm than good to the women participating in the program (Gøtzche, 2015, p. 344). However, the literature has produced vastly different estimates regarding the frequency of screen detected, overdiagnosed patients (Puliti et al., 2012, pp. 44-45).
1.5 Aim, relevance and use of this study
There is great uncertainty regarding the effect of mammographic screening, and by following the Norwegian Cancer Association’s suggestion of extending the target population, additional information should be available before final decisions are made. The object of this study is to further assess the impact of extending the target population of whom is offered to participate in the NBCSP based on the suggestion made by the Cancer Association. However, it is not within the aim of this thesis to solve the debate regarding overdiagnosis and the effect of screening. The debate is still ongoing and further research is needed. When considering implementing health care interventions, the cost-effectiveness is an important factor of the final decision. As no measures of screening´s true effect on breast cancer mortality and frequency of overdiagnosed patients are widely accepted, performing cost-effectiveness analysis, would not produce additional relevant information. This thesis approach the question of cost-effectiveness from a different angle compared to regular economic evaluations. The aim is not to assess whether the intervention is cost-effective, but estimate the minimum level of effect (risk reduction of dying) screening needs to achieve to be considered cost-effective.
The Cancer Association suggested extending the eligible target population from age 50-69 to 45-74. Age specific results in the literature is, however, often given by age 40-49 rather than 45-49. In addition, the effect of screening amongst women aged 40-49 has been estimated by the Research Council of Norway, thus already entered the political scene. Therefore, this thesis aims to provide further information for decision makers on extending the eligible age group from 50-69 to 40-69.
As the debate of breast cancer screening is still ongoing, and suggestions are made to further extend the eligible participant population and screening amongst 40-49 have already entered the political scene, the topic of this thesis is of timely relevance. By estimating the minimum
level of breast cancer mortality reduction screening needs to achieve to be considered cost- effective, it may be used as a reference point when considering the conflicting literature.
Additionally, knowing the impact of overdiagnosis on the minimum level of mortality reduction, may suggest on which platform or context overdiagnosis should be debated, e.g.
economical, ethical or both.
1.6 Presentation of the research question The research question of thesis is as follows;
- What is the minimum breast cancer mortality reduction amongst women aged 40-49 the Norwegian NBCSP needs to achieve, which makes extending the eligible
participant population from age 50-69 to 40-69 considered cost-effective?
The research question consists of a main and a sub question. The main question; What is the minimum risk reduction screening needs to achieve amongst women aged 40-49, for starting screening at age 40 rather than 50, to be cost-effective. Additionally, to answer this question, it implies a sub-question: how sensitive is the estimated minimum risk reduction to the uncertainty surrounding the topic of overdiagnosis in screening.
1.7 Thesis structure
The thesis is structured through four additional chapters. Chapter 2 provides the background and theoretical framework which is the foundation of the method applied. It describes the anatomical understanding of breast cancer, the theory of screening, features in different approaches of economic evaluations, and how the findings of this thesis will contribute to the existing literature. Chapter 3 explains the method applied, and chapter 4 presents the results.
Chapter 5 discusses the findings and how it compares to the literature, the strengths and weakness of the method applied, and a short conclusion.
2. Background and Theoretical Framework
This chapter provides the background and theoretical framework of which is the foundation of the method being applied in this thesis. Section 2.1 provides a detailed presentation of the anatomical features and classification of breast cancer. Section 2.2 provides the underlying theory of screening, including the aims and structural features. Furthermore, section 2.3 provides the basic information on health economic evaluation and modelling. These sections aim to increase the probability of a suitable method being applied.
Section 2.4 presents the existing literature regarding the effect (mortality reduction) and overdiagnosis as a result of screening. In additional, previous health economic evaluations conducted on breast cancer screening are presented, which will indicate the general
understanding on the cost-effectiveness of screening. Based on the information provided in section 2.1 – 2.3, possible strength and weakness of the existing literature are identified. Thus, indicates how the findings of this thesis may contribute the existing literature.
2.1 Breast cancer
The anatomic description is given from Store Norske Leksikon (online: https://snl.no) in the section of Store Medisinske Leksikon (Roald, Sauer, & Klepp, n.d.). Neoplasia, also called tumors, are an abnormal cell growth which can occur in the body´s organs and tissues. There are mainly two types of neoplasia; benign and malignant. The two groups of neoplasia differ mainly on benign neoplasia are cell growths in which grow slow and is expansive. However, as it grows towards the surrounding tissue, it will not grow into the tissue. Hence benign neoplasia may cause serious complications if it creates pressure on the surrounding tissue e.g.
in the skull (creates pressure on the brain). Malignant neoplasia, which is a synonym for cancer, does not stop growing and will grow into the surrounding tissue. Furthermore, the cells within the cancer tumor have the capability to break through the tissue, and into the lymph and blood system, enabling the cancer to spread to distant parts of the body. This process is called metastasis and is only possible for malignant neoplasia (cancer tumors).
There are many types of breast cancer which differ in both scope and severity. It is important to understand the different types as they will differ of both prognosis and the treatment options. The next sections, the difference between the types of breast cancers, the respective
characteristics and the meaning of cancer staging will be described, all of which are important to understand a cancer diagnosis.
2.1.1 Types of breast cancer
Breast cancer can be organized as seven different types of breast cancer; Carcinoma in situ (CIS) (mainly Ductal Carcinoma in Situ (DCIS)), Invasive breast cancer (IDC/ILC), Triple Negative Breast Cancer (TNBC), Inflammatory Breast Cancer (IBC), and less common types such as, Angiosarcoma, Phyllodes tumor and Paget disease of the breast (American Cancer Society, n.d. a, p. 1). The different types of cancer differ in the location of the primary lesion and the ability to spread to the surrounding tissues. However, a cancer tumor may progress to later stages despite the initial identification and classification. E.g. a CIS may progress and be reclassified as an invasive cancer at a later point in time. Figure 2.1 gives the anatomy of the breast to give a visual context to where the initial cancers are located.
Figure 2.1: Anatomy of the breast. (Note Reprinted) from [Untitled image of the anatomy of the female breast][Image].(n.d.). Retrieved from: http://bccr.unmc.edu/about_cancer.html Ductal Carcinoma in Situ (DCIS)
CIS can both appear in the milk ducts and lobules, however as mainly DCIS is the most common CIS, Lobular Carcinoma In Situ (LCIS) will not be described. DCIS starts to appear within the milk ducts of the breast, however is not yet cancer. In the Store Medisinske
Leksion, DCIS is defined as a precursor to cancer, where the abnormal cells have not been able to penetrate the duct cells and further out to the breast tissue (Klepp, n.d.). DCIS used to only make up a small part of new breast cancer diagnosis (1-2%), however after the
introduction of breast cancer screening , DCIS makes up approximately 20% of new breast cancers detected (Barrio & van Zee, 2017, p. 198). Currently, DCIS has excellent prognosis with a 5-year survival rate close to 100% (Narod, Iqbal, Giannakeas, Sopik & Sun, 2015, p.893)
Even though it is not classified as cancer, there is a probability that DCIS may eventually penetrate the duct cells, hence be reclassified as an invasive cancer. Thus, even though DCIS is not classified as a cancer, it will still be treated if detected. However, there are no good or effective methods to separate non-invasive with pre-invasive tumors, which in turn is the key feature in the context of overdiagnosis and overtreating in screening (see section 2.2.2) (Paci
& Duffy, 2005, p. 266).
Invasive breast cancer (IDC and ILC)
If the tumor has spread to the surrounding breast tissue, it is classified as invasive breast cancer (American Cancer Society, n.d. c, p. 7). Most invasive cancer is a result of DCIS and LCIS developing into Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) respectively. Together they make up approximately 70-80% of all breast cancers (American Cancer Society, n.d. b, p. 3), where IDC and ILC makes up 8 and 1 in 10 of all invasive breast cancer respectively. With the cancer becoming invasive the survival rates decreases. In addition there is a danger the cancer will further expand to other organs and structures in the body, i.e. become a metastic cancer.
The main difference between IDC and ILC is the location at which the primary tumor started to appear. As IDC and ILC is a result of DCIS and LCIS spreading to the surrounding tissue respectively, IDC starts in the milk duct, and LCIS starts in the lobules. The American Cancer Society also shows to a higher risk of the ILC patients being detected by having cancer in both breasts compared to IDC.
Other types of Invasive breast cancer
The breast cancer types mentioned above are the most common types of breast cancers.
However, there are other types based on other cancer characteristics. Triple negative breast
cancer (TNBC) is an invasive cancer which makes up about 15% of all breast cancers (American Cancer Society, n.d. d, p.10). The key characteristics of TNBC are the cells incapability produce enough HER2 protein, estrogen and progesterone receptors, where the cells test negative on all three tests. The American Cancer Society further states, due of the cells´ characteristics, the treatment options are affected and with faster growth rate compared to other invasive cancers, TNBC has worse survival prognosis.
Inflammatory Breast Cancer (IBC) is a rare breast cancer which only accounts for 1-5% of all breast cancer, and spreads in similar rate as TNBC (American Cancer Society, n.d. e, p. 13).
The characteristics of IBC are the symptoms, outlook, and treatment of which differs compared to other types of breast cancer. It is called inflammatory due to cancer cells blocking lymph nodes, which in turn makes the breast look inflamed.
Three other rare types of breast cancer are Angiosacoma, Phyllodes tumor and Paget disease of the breast (American Cancer Society, n.d. b, p. 4). These types of breast cancer will only be mentioned without further description as these cancers are very rare, and would not have great impact on the choice of method being applied.
2.1.2 Classification of cancer diagnosis – The TNM system
The type of breast cancer does not provide sufficient information to clinicians, as within the cancer types, characteristics of the specific tumors may differ. Breast cancer grades and breast cancer staging provides information on the growth rate and the potential the tumor might spread (American Cancer Society, n.d. f-g, pp. 34 - 36). Breast cancer grades are given by a number from 1 to 3, where a higher number indicates a fast tumor growth rate and more potential to spread. Breast cancer stages is given by a number from 0-4, where higher number indicates more spread, thus a more severe cancer with worse survival prognosis.
The most common staging system of breast cancer is the TNM staging system develop by the American Joint Committee on Cancer (AJCC) (National Cancer Institute, 2015). The staging system takes into account the size of the tumor (T), the spread to nearby lymph nodes (N) and whether metastasis have taken place (M). Within these categories there are multiple levels of which are followed by numbers or letters to give further information about the cancer. The following tables are based on the overview provided by the American Cancer society (n.d. g).
Detailed information regarding the tumor size, T, is followed by a number (0-4), or letters, where higher number indicates larger primary tumor size or spread to the tissues and letter(s) provides additional features of the tumor. Table 2.1 provides the level within the category T.
The details of category N are describing the spread to lymph nodes located close to the breast.
Similar to the previous category, N is followed by a number (0-3) indicating how many lymph nodes are affected by the spread and/or letters which provides additional information. Table 2.2 provides an overview of the levels within category N.
Category M (metastases) describes whether the cancer has spread to distant organs or structures. M is followed by either 0 or 1 indicating whether metastases is observed or not.
Additional, letters gives further details if metastasis could not be confirmed/denied sufficiently. Table 2.3 gives an overview of the levels within category M.
Combining the categories (with the associated levels) e.g. T2N1bMX, gives a more specific description of the cancer´s characteristics. However, considering all the possible combinations across the categories and the respective levels, comparing breast cancer diagnosis may be difficult on a general basis.
When T, N and M categories are assigned, it is given roman number (I – IV) representing the clinical stage (Felleskatalogen, n.d.). The numbers represent groups in which the higher the number indicates larger tumor size and more spread. Thus, higher number equals more severe cancer. The clinical stages makes it possible to compare cancers across TNM category-level combinations, i.e. generalized groups. For instance, if a cancer is diagnosed with TNM I the survival prognosis is better compared to TNM III (Småstuen, Aagnes, Johannesen, Møller &
Bray, 2008, p. s16). Table 1.4 shows the respective TNM stages with a following description of the cancer´s characteristics at the respective stages. The table is based on information given
by Felleskatalogen (Felleskatalogen, n.d.). Felleskatalogen does not include in situ (stage 0), however in situ will be included in the table as in situ is often included elsewhere (National Cancer Institute, 2015). Additionally, due to detection rate of in situ during screening and its link to overdiagnosis, it is a feature that will be accounted for in the applied method.
2.2 Screening theory
Screening has become a popular intervention in medicine and public health (Aschengrau &
Seage, 2014, p. 418). It can be defined as an examination of healthy individuals using a standardized examination method to detect a yet undiscovered disease or risk factor (Braut, n.d.). It is not within the aim of screening to diagnose, merely identify individuals where there is a high probability of the disease being present. These individuals are given further
examination, before a potential (over)diagnosis and (over)treatment. Figure 2.2 illustrates the process and aim of screening, and the location of potential overdiagnosis.
Figure 2.2: Illustration of screening, inspired by The Screening Process. From “Essentials of Epidemiology in Public Health (ed. 3)”, by A. Aschengrau & G. R. Seage, 2014, p. 418, Copyright 2014, Jones & Bartlett Learning, LLC, an Ascend Learning Company.
The aims of screening are to increase survival and to reduce side effects through detecting the disease in earlier stages. This builds on the assumption that detecting the disease at earlier stages results in more effective treatments, which in turn increase survival and/or reduce side effects (Aschengrau & Seage, 2014, p. 418). After potential patients are detected through screening, they are given further examination before treated if diagnosed.
With screening, detection of cancer can be divided in three groups: Cancer patients who did not participate in screening, but diagnosed through clinical symptoms (no-attenders/outside screening), patients who were diagnosed after being identified as possible cancer patients through a screen (screen detected), and individuals who participated and had their cancer detected between screens (interval cancer) (van Bommel et al., 2017, p. 2). The type of detection provides a map of which the effect of screening can be located. Breast cancer cases which are diagnosed outside the screening program or as an interval cancer, will not be cases in which screening had an impact. Even though patients diagnosed between the screens do participate, the detection of their cancer is not a result of screening. Hence, the true impact of screening on breast cancer survival, is located amongst screen detected patients.
Sensitivity and specificity are relevant features of screening. Sensitivity and specificity of the Norwegian NBCSP have been reported to be 90.6 % (74.6%) and 99.5% (99.5%) at one (two) year follow-up (Hofvind, Geller, Skelly & Vacek, 2012, pp.1229-1230). However will not be within the framework of this thesis as the main topics in the debate of screening are risk reductions and overdiagnosis.
2.2.1 Length and Lead time bias
Screening is associated with multiple biases, that may lead to false estimation of survival rates (Cox & Sneyd, 2013, pp. 1042-1043). Lead and length time bias are well known biases
occurring when screening is applied, and have an favorably impact towards screening.
Lead time bias produces a potential overestimation of cancer survival through cancers being detected earlier, however the time of death would not change. As the cancer has been detected earlier, the patients are recorded to live with cancer for a longer period of time, than if the cancer would be detected clinically (detected by symptoms). The additionally recorded
survival is defined as lead time. Figure 2.3 a provides a graphical illustration of lead time bias.
Figure 2.3 a: Graphical illustration of lead time bias
Length time bias appears due to different characteristic of the tumors detected. The
aggressiveness of a tumor, affects the time of which the tumors are detectable with screening.
Screening is more likely to detect slow growing than fast growing tumors, hence would provide false information regarding the cancer survival due to better survival prognosis
amongst the slow growing tumors. Figure 2.3 b provides a graphical illustration of length time bias.
Figure 2.3 : Graphical illustration of length time bias.
With both lead time and length times bias present, there will be a possibility that the existing estimates of the survival benefit from screening is false. Further, overdiagnosis is an extreme example of lead time and length time bias, and will therefore be presented in detail in the next section.
2.2.2 Overdiagnosis
Overdiagnosis could be considered a flued term as it is broadly used. However, overdiagnosis should not be misunderstood as a false-positive result or a misdiagnosis, nor is it a synonym of overtreatment or overtesting (Brodersen et al. 2018, p.2). Hofmann (n.d.) defined
overdiagnosis as: “… the diagnosis of a medical condition that would not lead to ailments, developed or death if it had remained undetected”. This definition is applied in this thesis. As seen in figure 2.2, the patients do have a clinical detectable disease, however, the disease would not impact patients´ life quality, nor would the patients die as a result of the disease.
This implies, based on the biases presented in the previous section, screening would extensively benefit from overdiagnosed patients. As the disease would be detected early it would contribute to increased recorded survival time.
In cancer, the overdiagnosed tumors are characterized as being non-aggressive. They do not further progress, or they progress slowly to the degree in which they would not have an impact throughout the patients´ life time. There are, however, currently no sufficient methods to identify and differentiate between which tumors have the characteristics of an
overdiagnosed or an aggressive cancer tumor (Paci & Duffy, 2005, p. 266). Hence, all detected tumors need to be approached similarly, with invasive treatments, regardless of identical patient and tumor characteristics.
Even though there are no available indications to differentiate the tumors, many attempts have been made to estimate the proportion of overdiagnosed patients as a result of screening. The formula is often given by the difference between the number of patients diagnosed through screening (A) and no-screening group (B), divided by the occurrence of breast cancer without screening (C) (See formula below). However, there is a debate regarding how to define the patients in group C (Hofmann, n.d.)
𝐴 − 𝐵 𝐶
By using this formula, per definition, breast cancer would only appear as a result of screening, which means overdiagnosed patients only appear amongst the screen detected cancer patients.
I.e. overdiagnosed patients are excluded from the pool of interval cancer and non-attenders.
2.3 Health economic evaluation
The coming sections provide the basic features of health economic evaluation of health care interventions. The section presents the underlying aim of economic evaluations in health care, possible approaches to conduct an economic evaluation, and health economic modelling with the associated strengths and weaknesses. The aim of these sections is to identify, based on the section 2.1 and 2.2 which approach of economic evaluation and health economic modelling are suitable to answer the research question.
The costs of health care are increasing. The reasons are many, such as an increasing
population, higher numbers of elderly within the population which requires more health care services, diseases such as cancer which are difficult and/or impossible to fully cure, and new expensive health care technology. Hence, with scare resources, questions arise on how the resources are to be spent. Suggestions such as extending the eligible population for
participation of the NBCSP, needs to be evaluated as the required resources could be spent elsewhere, on other public health interventions. The aim of economic evaluation in health care is to identify which strategies in question will produce the highest net benefit by spending the associated resources.
Economic evaluation has two main features (Drummond, Schulper, Claxton Stoddart &
Torrance, 2015, pp. 3 - 4 ). Firstly, it has a focus on the inputs and outputs, i.e. the costs and benefits. Second, economic evaluation tries to identify and make explicit criteria which reflects the social values when decisions are not made on our own behalf, but on others.
Drummond et al. (2015), explains that based on these two characteristics, economic
evaluation can be defined, as they write: “the comparative analysis of alternative courses of action in terms of both their costs and consequences.”. In simplified words, economic evaluation analysis the cost and consequences of a new option (intervention) by comparing it to the cost and consequences of a comparator. The use of comparators separates economic evaluations from regular cost-analyzes. By using comparators, the results are given as the relationship between cost and outcomes of the intervention and comparator, thus providing information regarding the cost-effectiveness of a specific course of action. Figure 2.4 below shows a simplified, graphical illustration of the underlying method of all economic
evaluations.
Figure 2.4: Illustration of the underlying method of economic evaluation It is important to note that the choice of comparator is a key element for an economic
evaluation to produce valuable information for the decision makers. The cost-effectiveness is only a relationship between the intervention and comparator, hence by using different
comparators, the cost-effectiveness of the intervention may be misleading. Drummond et al.
(2015) states, that: “. . . any alternative can look ´good´ when only compared to something that is sufficiently ´bad´”. Most often, what the intervention will be replacing if implemented is used as comparator, which could also be the absence of any treatment. Similarly, if other interventions are available they should all be considered as the cost-effectiveness between the interventions could differ as well.
2.3.1 Different approaches of economic evaluation
Even though the basis of all economic evaluation are the same as shown in figure 2.4, there are many approaches to perform an economic evaluation. The four main types are; Cost- Minimization Analysis (CMA), Cost-Benefit Analysis (CBA), Cost-Effectiveness Analysis (CEA) and Cost-Utility Analysis (CUA) (Drummond et al., 2015, p. 11). Common for all, the analysis are based on the same underlying characteristics, however the analysis may be based on different assumptions or differ in the measure of outcome/consequences. Hence, by evaluating specific options some analysis are better suited.
CMA could in many ways be seen as a cost-analysis. CMA are based on options where the consequences in terms of effectiveness between the intervention and comparator is equal (Drummond et al., 2015, p. 6). Hence, the preferred strategy, is the strategy with the smallest associated costs. However, the applicability of CMA is questionable. Often the effects and costs of a certain option are based on estimates, which in turn are uncertain, hence the requirement of equal effectiveness would not hold. Additionally, the intervention and comparator are often expected to differ in terms of effectiveness, thus other approaches are more appropriate.
CBA is characterized by converting all costs and consequences in monetary values, and the net sum of the cost and consequences is analyzed (Morris, Devlin, Parkin & Spencer, 2012, pp.233-234). Positive (negative) net sum indicates higher (less) benefits of the examined strategy over the comparator. CBA has the ability to compare strategies in which the measure of inputs and consequences differs, e.g. cross-sector strategies. However, it requires that all cost and consequences are able to be monetarized (Morris et al., 2012, p. 234). When the market price is not available, e.g. in health, the monetary value of e.g. a health condition is found by individuals expressing hypothetical willingness-to-pay (or accepting compensation, WTP and WTA respectively) for a specific health state (Drummond et al., 2015, p. 10).
CEA produce an Incremental Cost-Effectiveness Ratio (ICER) (often cost per unit gained).
(Drummond et al., 2014, p. 5). E.g. cost per reduced delayed day of surgery for a specific disease. As the effectiveness of the strategies of interest may differ, opposite from CMA, the less costly strategy is not necessarily the most preferable strategy, as additional increase in effect may outweigh the increase in costs. However, CEA is limited and not suitable to compare options where the respective courses of action handles different disease. For
instance, if the outcome of measure is life years gained, and two options affecting cancer and cardiovascular diseases are considered, the life years gained will be equally valued even though the value of the health state in the respective options differ (10 years of cancer may not be equally valued to that of cardiovascular disease).
CUA are in many ways equal to CEA, however, is able to compare options affecting different diseases simultaneously (Drummond et al., 2015, p. 8). Where CEA reports the cost-
effectiveness as an incremental cost per specific unit, CUA produce an incremental cost per health unit gained. The health unit is often defined as a QALY. QALY is the number of life years which are weighted from 0-1 based on the HRQoL of which the individual experiences at a given health state (where 0 is dead and 1 is perfect health). E.g. 10 years of breast cancer would be valued less compared to 10 years in perfect health.
2.4 Health economic modelling
To perform an economic analysis, health economic modelling is often required. The models are designed based on the inputs, and produce expected consequences of the intervention and comparator. The most common models are Markov and Decision Tree (see figure 2.5 a and b for a graphical illustration ).
Figure 2.5 a: Markov model Figure 2.5 b: Decision tree model The Markov model is a health state model of which the individuals may transfer between multiple health states (Briggs, Schulper & Claxton, 2006, p.30 ). The arrows in figure 2.5 a represent transition probabilities, which are probabilities of transitioning between the respective health states. The model further consists of cycles. The cycle is a time measure, e.g. 1 year, where in each cycle the individuals transition between the health states. At the respective health states, the individual experience costs and outcomes which is recorded for each cycle and summed. To calculate the cost-effectiveness, the cost and outcomes are calculated for the comparator and intervention arm respectively, and presented as an ICER.
The decision tree have previously been stated to probably be the simplest form of a decision model (Briggs et al., 2006, p. 23). The patients follow different pathways based on their respective prognosis with associated probabilities, costs and outcomes (Drummond et al., 2005, p. 327). The decision tree model however, may quickly become very complex as time horizon increases. E.g. in screening, a decision tree model could be argued to be too complex, as overall survival are the measure on effect of screening, thus with screening appearing mid- life, the patients are followed over a long time period (e.g. over 50 years).
2.4.1 Model uncertainty
All relevant costs and consequences should be included in the analysis, and model parameters are often estimations. With estimations, there are uncertainties attached to the parameters, known as parameters uncertainty (Briggs et al., 2006, p. 19). If a decision maker base a decision only on the economic evaluation using deterministic values, there is a chance the wrong decision will be made. To reduce the likelihood of wrong decision being made,
multiple analysis can be conducted; Deterministic- and Probabilistic Sensitivity Analysis, and Value of Information analysis.
Deterministic Sensitivity Analysis (DSA) assesses how a change of a single assumption or changing a scenario will affect the results (Simoens, 2009, p. 2960). How would the results change if the assumption of a certain discount rate was increased or decreased? Or if the available literature are disagreeing of an essential model input parameter, how will the
different parameter values change the results? This information will be useful as it informs the decision maker whether the results is highly dependent on specific model parameters or assumptions. However, DSA is not able to assess the impact of the uncertainty across all parameters.
Probabilistic Sensitivity Analysis (PSA) has become the norm in handling parameter
uncertainty (Simoens, 2009, p. 2960). Where DSA focuses on a single assumption or a single scenario, PSA draws a random value from the respective parameters´ appropriate distribution, otherwise known as a Monte Carlo simulation. The process is completed e.g. 1000 times (1000 iterations), to indicate of how strongly the parameter uncertainty impacts the model results. Through a Cost-Effectiveness plane (CE-plane), the incremental cost and outcomes of all iterations are graphically illustrated. The more spread between the cost and outcomes of the iterations, the more uncertain are the results and vice versa. By calculating the Net Monetary Benefit (NMB) for the respective iterations, the different strategies’ probability of being cost effective at given WTP – per QALY gained thresholds are visualized through a Cost-Effectiveness Acceptability Curve (CEAC). However, the CEAC does not show what would be the optimal choice of the respective strategies, rather only which strategy has the highest probability to be the cost-effective strategy at given thresholds. I.e. the most optimal choice does not necessarily have the highest probability of being cost effective. As a result, the decision makers should not base their decision alone on the CEAC, but also considered what is the most economical optimal strategy. Based on the respective NMBs, a Cost-
Effectiveness Acceptability Frontier (CEAF) present the strategy with the highest NMB at given thresholds. The PSA, inform the decision makers on the impact of the uncertainty within the model parameters, but it may not be sufficient, especially if the final results are balancing around the WTP per QALY gained threshold. To further reduce the probability of the “wrong” decision being made, further research may be conducted to reduce additional parameter uncertainty. Value of Information (VoI) analysis informs the expected gain of conducting further research to reduce or eliminate uncertainties (Fenwick et al., 2020, p. 139).
If the expected gain surpass the cost of research, the decision should be delayed until further uncertainties are reduced/eliminated. If the expected gain of research is less than the cost, the decision should be made based on the current knowledge. VoI analysis can be conducted for different level of uncertainty.
Expected Value of Perfect Information (EVPI) is the expected gain of eliminating all uncertainties. EVPI is given by the difference between the NMB (calculated as part of the Monte Carlo simulation in the PSA) with current and perfect information (Rothery et al., 2020, pp. 279-280 ). NMB with current information are represented by the strategy with the highest, average NMB of all iterations at the given threshold (the maximum of the expected NMB). NMB with perfect information is given by the average of the highest NMB between the strategies at each iterations (the expected maximum NMB). Further, by multiplying the EVPI with the population (pEVPI) it should in theory provide the expected gain of
elimination all uncertainty across all individuals affected by the intervention.
𝐸𝑉𝑃𝐼 = [𝑁𝐵𝑑(𝜃)]] − 𝑚𝑎𝑥
𝑑 𝐸𝜃[𝑁𝐵𝑑(𝜃)] 𝑝𝐸𝑉𝑃𝐼 = 𝐸𝑉𝑃𝐼 ∗ ∑ 𝐼𝑡
(1+𝑟)𝑡 𝑇𝑡=0
𝜙 = the parameter of interest 𝐼𝑡 = Incidence time period t 𝜓 = other uncertainty r = discount rate
𝜃 = vector of unknown parameters T = time horizon 𝑑 = decision options
Expected Value of Perfect Parameter Information (EVPPI) is the expect gain of
reducing/eliminating all the uncertainty of specific parameters or parameter groups. EVPPI is given similarly to EVPI, however consist a double Monte Carlo simulation, an inner and outer loop (Rothery et al., 2020, pp. 279-280). The inner loop draws a random value from the appropriate distribution(s) of the parameter(s) in question and keeps the parameter value fixed while varying the remaining parameters for S samples, and where the average NMB of each sample is stored. The outer loop repeats the inner loop for K samples and stores the average NMB of each sample. The NMB with current information is given by the NMB of the optimal
strategy based on the average NMB of the K samples. The NMB with perfect information is the average the maximum NMB between each strategy at each K sample.
𝐸𝑉𝑃𝑃𝐼𝜙 = 𝐸𝜙[𝑚𝑎𝑥
𝑑 𝐸𝜓|𝜙[𝑁𝐵𝑑(𝜃)]] − 𝑚𝑎𝑥
𝑑 𝐸𝜃[𝑁𝐵𝑑(𝜃)]
𝜙 = the parameter of interest 𝜓 = other uncertainty 𝜃 = vector of unknown parameters
𝑑 = decision options
2.5 Existing literature on mammographic screening of women in the age interval of 40-49
This subsection presents the existing literature regarding breast cancer screening in the age interval of 40-49. The main relevant literature concerning this thesis topic includes effects, overdiagnosis and the cost-effectiveness of screening. First, the preliminary search will be given, and the most relevant articles will be presented and discussed. Lastly, based on the existing literature, the contribution of the thesis will be presented.
2.5.1 Preliminary search
The existing literature of the population based breast cancer screening program are
comprehensive. Two general preliminary searches were conducted using PubMed, Google Scholar, Embase and Cochrane Library. The first general search focused on the cost- effectiveness analysis of breast cancer screening. Search terms such as “Cost effectiveness analysis” and “Mammography screening” were used, and gave approximately 92 000 hits.
With the search terms of “Overdiagnosis” and “Mammography screening” it gave
approximately 13 000 hits. Even though not all articles are cost-effectiveness analysis, or overdiagnosis relevant articles, the number of hits shows how well the topic of breast cancer have previously been described. However, as the aim of this study is breast cancer screening of women aged 40-49 the search terms needed to be adjusted. The major key word to be included was “age 40-49”. In addition, as the research question is focusing on the Norwegian NBCSP, the search also includes Norwegian translations to identify possible articles written in Norwegian. Variations of the key words, e.g. “breast cancer screening” could be changed to
“mammographic screening”, or “Cost-effectiveness analysis” changed to “CEA”. The access of the articles (online) were available through the University library of Oslo (available at ub.uio.no).
Table 2.5 gives an overview of 8 relevant articles (ranked in no specific order) with regarding the effect, overdiagnosis and cost-effectiveness of mammographic screening. The relevance of the articles were based on three categories;
1. When was the article published? Older publications may be outdated, hence newer articles are preferred.
2. Where was the study/trial/report conducted? The study should be conducted in a context of what is similar to the Norwegian NBCSP and with Norwegian patient characteristics.
3. Is it a general overview? Systematic reviews or meta-analysis are preferred over single reports, as it presents multiple articles on the specific topic.
Fairly recent publication concerning the topic were available. No articles were found which estimates the minimum level of effect screening needs to achieve for it to be considered cost- effective. The effect of mammographic screening of women aged 40-49 was the most frequent topic. There were few cost-effectiveness analysis which specifically looked at age 40-49 or 40-69 (which would be the result of including women aged 40-49 to the current
eligible participation population). As only few papers were found regarding screening women aged 40-49 or screening in a Norwegian context, similar western societies such as USA, UK and Sweden were chosen. In addition, no papers were found regarding estimates of
overdiagnosis amongst women aged 40-49 specifically. Thus, the articles regarding overdiagnosis are not age specific.
2.5.2 Presentation of relevant articles
This subsection presents the findings from the literature search, and identifies a gap in the literature where the findings of this thesis may contribute. The relevant articles of the topic were divided in three groups; articles studying the mortality reduction/survival benefit, overdiagnosis and the cost-effectiveness of screening.
Effect (morality reduction) of screening amongst women aged 40-49
Ray, Joe, Freimains, Sickles & Hendrick (2018) summarized the current evidence regarding mammographic screening of women age 40-49. They based their conclusion on multiple randomized control trials (RCT), observational studies and model estimates, which in turn reported 15-48% reduction in breast cancer mortality. The paper also showed reduction of recall rates and an annual screening rate that is more effective than the current, biennial NBCSP in Norway. However, Ray et al. (2018) did indeed seem to focus more on the benefits of breast cancer, and, to some extent, trivialize the negative sides of mammographic
screening. E.g. RCTs showing no significant reduction of breast cancer mortality was seen as having substantial flaws (mainly the Canadian National Breast Screening Study (CNBSS-1)), and overdiagnosis, as they write: “ . . . should not factor into decisions regarding the
appropriate age to begin screening or screening interval.”. They justify their conclusion on overdiagnosed patients only make up 10%, and overdiagnosis is more prevalent in older compared to younger age groups.
van den Ende, Oordt-Speets, Vroing & van Agt (2017) did not recommend mammographic screening of women age 40-49 based on the current evidence. van den Ende et al. (2017) conducted a systematic review of the current literature. Their conclusion was based on four studies, one in which (CNBSS-1) was rejected by Ray et al. (2018) due to flaws in the study design (the weakness of CNBSS-1 was also acknowledged by van den Ende et al. (2017)).
They report no/small and a non-statistical significant effect of reduction in breast cancer
mortality. Overdiagnosis was also reported to be considerably higher compared to Ray et al.
(2017), 41% and 55% for 5 years- and 20 years post-cessation of screening respectively.
The Norwegian report by Bjørndal & Forsetlund (2007), dated back to 2007, and may be outdated compared to the two reviews presented above. The report based their conclusion on three previous conducted reviews (US Preventive Services Task Force (USPTF) in 2002, International Agency for Research on Cancer (IARC) in 2002, and Cochrane Collaboration in 2006). On average, the three reviews reported the same average absolute risk reduction of 0.1%. However, the report assessed the average absolute risk reduction estimated by the reviews to be slightly optimistic, and stated it is more likely the effect of screening women aged 40-49 would be 0.03%. Compared to Ray et al. (2018) the Bjørndal & Forsetlund (2007) seemed to acknowledge the negative sides of screening to be of higher importance, where mainly overdiagnosis was the important issue.
The reported effect of screening amongst women aged 40-49 years varies. Three reviews of the current evidence of which one is highly positive, whereas the remaining two reported less effect form screening and suggest caution should be made regarding the question of
implementing screening within this age group.
Overdiagnosis
Puliti et al. (2012) performed a literature review of the conducted estimates of overdiagnosis in mammographic screening for breast cancer across Europe. The review emphasized the extent of the conflicts in the literature concerning the number of patients overdiagnosed as a results of screening. When combining CIS and invasive cancer, the estimates of overdiagnosis ranged from 0.86 - 57%, if only invasive cancer was considered, 0.86 - 54%. The estimates based on the Norwegian screening program were estimated to be 54% and 52% (assuming 10% CIS) by Zahl, Strand & Maehlen (2004) and Jørgensen & Gøtzsche (2009) respectively.
The estimations of overdiagnosis in the Norwegian context presented by Puliti et al. (2012) are fairly similar (52% and 54%), compared to estimations performed outside the Norwegian context (1% - 54%). However, Lund, Nakamura & Thalabard (2018) reported estimations of overdiagnosis in the Norwegian NBCSP which contradicted the previous estimates. They reported estimations that were non-significant and concluded that there were no
overdiagnosed patients in the NBCSP.
As there is currently no widely accepted method to estimate the frequency of overdiagnosed patients, it is difficult to argue whether the differences in estimated overdiagnosed patients are a result of using wrong methods, or if a certain method produces more realistic result. It is likely that the existing literature will remain in conflict until a single method is widely approved.
Cost-effectiveness of mammographic screening amongst women aged 40-49 (40-69) There were few available CEAs of mammographic screening in which women aged 40-49 were included. None of which were in the context of the Norwegian NBCSP. The results of these analysis are therefore likely not to be transferable to a Norwegian context. Differences in medical costs such as drugs and treatment, screening- and patient characteristics, may vary across borders which in turn affects the results. However, even though they would not be directly transferable, the results could give a pointer towards the cost-effectiveness of screening amongst the age group of 40-69. It is therefore important, to consider whether the underlying models used are applicable to a Norwegian context. Three CEAs of
mammographic screening, containing separate cost-effectiveness results of the eligible age group 40-49, were found. Two of the CEAs reports the cost-effectiveness as cost per life-year gained, however the preferred measure of cost-effectiveness in Norway is cost per QALY gained (The Norwegian Medical Agency, 2018, p 28).
In 2013, a program-specific CEA was conducted by Melnikow et al. (2013). The CEA estimated the cost-effectiveness of breast cancer screening in different frequencies and age groups, where no screening was the chosen comparator. Multiple screening frequencies, and populations were considered. Screening from age 40 however, was not considered cost- effective, as screening from age 50 was the optimal choice. There are many reasons why the results are not transferrable to the Norwegian context. Firstly, the analysis is not representing what would be the eligible population of participation in the Norwegian NBCSP. The CEA analysis reported the cost-effectiveness of screening in the age of 40-64, however in the Norwegian NBCSP it would be 40-69. Secondly, there were issues with the model design.
The cost-effectiveness was presented in life years, when QALYs are the preferred measure of outcome. Additionally, as mentioned in section 2.1-2, the breast cancer stages were
emphasized to be of importance to capture the true effects of screening. Parameters such as costs and utility weights should be stage specific as screening would not have a direct impact on the outcomes after it has identified the possible cancer tumors. The CEA used stage-