Master thesis in Health Economic Evaluation
A Budget Impact Analysis of
The Norwegian Cervical Cancer Screening Programme:
Cytology- vs.
HPV based primary screening
Thesis submitted as a part of the Master of Philosophy Degree in Health Economics, Policy and Management
UNIVERSITY OF OSLO
The Faculty of Medicine
Department of Health Management and Health Economics Stein Son Hoang Nguyen
Supervisors: Eline Aas & Kine Pedersen
November 15th 2017
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A Budget Impact Analysis of
The Norwegian Cervical Cancer Screening Programme:
Cytology- vs.
HPV based primary screening
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© Stein S. H. Nguyen 2017
The Norwegian Cervical Cancer Screening Programme: Cytology- vs. HPV based primary screening
Stein S. H. Nguyen
http://www.duo.uio.no/
Publisher: Reprosentralen, University of Oslo
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Abstract
Background:
The cervical cancer screening program in Norway is introducing HPV detection as the primary test. HPV is more sensitive compared to cytology and is expected to improve detection against cervical cancer. This will refer to an increased number of women requiring further follow-up visits like re-testing and treatment, and thereby, an initial increase in budget and resource use will follow. Because of this increment, prevention can be performed at earlier stages than anticipated and healthier screening population will advance. For this purpose, primary HPV testing is predicted to reduce additional interventions in a long-term perspective. Our aim was to assess the clinical and budgetary impacts of the new suggested strategy compared to today’s screening program in Norway.Method:
A decision-tree model was used to estimate clinical and cost implications for screening diagnosis of cytology and HPV-test, in addition to cervical biopsy within a 15-year framework. Initial screening results and follow-up data from 31 655 women in the age 34-69 years in four Norwegian counties were used for developing the model. Costs in the analysis were related from a third-payer perspective and were calculated using different payment schemes. Outputs from the model were to obtain detected cases of diagnosis referred as CIN2+, total costs, as well as resource use in form of tests, colposcopies with biopsies and conizations.Results:
Results were given in two scenarios, in case of unadjusted and adjusted positivity rates. In terms of resource use, adjusted HPV screening triggered the least utilization, except for biopsies. Based on the BIA, the recommended intervention reduced a total cost from 66/68 mill NOK to 49/52 mill NOK (22/26 % reduction) in unadjusted/adjusted setting compared to the current strategy. Based on the CEA, cytology screening detected 889/980 and HPV screening 830/901 CIN2+ cases after 15 years. On average, the recommended intervention detected 40% more CIN2+ cases, with an increase from 0.0112/0.0185 to 0.0124/0.0202 in unadjusted and adjusted setting respectively. With a WTP threshold for an additional CIN2+detected above 45 500 NOK, primary HPV detection can be considered cost-effective.
Conclusion:
The results inform that primary HPV screening is more affordable and produces better clinical outcomes in the long run. However, it must be prepared for an increase resource use and workload. The strategy can be considered cost-effective if the WTP threshold meets the ICER, which should be achievable in the Norwegian healthcare system. If not, there is potential for further research given the feasible value from the VOI analysis.- 6 -
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Acknowledgement
After an internship in the fall 2016 for the Cancer Registry of Norway, I was fortunate to get involved with the cervical cancer screening program and work with some amazing enthusiastic people. During my stay, I found the topic interesting, which made it reasonable for me to pick my topic for the thesis.
I am truly thankful to Birgit Engesæter, Adviser at the cervical cancer screening section who made it all possible and giving me the opportunity. You have been supporting me from day 1, reflecting on related issues with me, providing me with information, data and so much more.
A great appreciation to the leader of the screening program, Ameli Tropé, who greatly receipted me, filled me up with important inputs and expert opinions. Also, thanks to Special Adviser Gry Baadstrand Skare, who has been involved since the start of the national screening program, for sharing your experience and knowledge.
I would then like to show gratitude to Associate Professor Eline Aass, my supervisor, for your guidance and enlighten me with ideas. With you also as my teacher, I greatly benefited from the modeling courses for this thesis. Thank you, Kine Pedersen, my co-supervisor and currently PhD candidate related to cervical cancer screening and economic evaluation, for presenting me your work, supplying me with literatures, giving me advices throughout and also suggestions on what to research on within the field.
Finally, a huge attention must also go to my friends and family for encouraging me and making my days brighter when needed. Especially, I want to highlight two friends; Håvard Haugnes and Svenn Alexander Hansen. Thank you for 5 educational years together. Having you alongside the education has made the journey as a student so much easier and I am forever grateful.
Best,
Stein S. H. Nguyen November 2017
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Content
1 Introduction ... 1
1.1 Aim of the study ... 2
2 Background ... 5
2.1 Clinical background ... 5
2.1.1 Cervical cancer screening ... 6
2.1.1.1 Cytology testing ... 8
2.1.1.2 HPV testing ... 9
2.1.2 Cervix cancer in Norway ... 12
2.1.2.1 Current prevention ... 12
2.1.2.2 Future prevention ... 13
2.1.2.3 Primary cytology screening – todays national strategy ... 14
2.1.2.4 Primary HPV screening – the recommended intervention ... 15
2.1.2.5 Follow-up ... 15
2.1.2.6 Unsatisfactory results ... 16
2.2 Theoretical background ... 17
2.2.1 Criteria for prioritization ... 17
2.2.2 Economic evaluation ... 18
2.2.3 Cost-effectivenes analysis ... 18
2.2.4 Decision analytic modeling ... 19
2.2.5 Budget impact analysis... 21
2.2.6 Micro- & case-mix costing ... 22
2.2.7 Uncertainty analysis ... 23
2.2.6.1 Probabilistic sensitivity analysis ... 24
2.2.6.2 Value of information ... 25
2.2.6.3 EVPI, PEVPI & EVPPI ... 26
3 Method and materials ... 29
3.1 Overview of the study ... 29
3.3.1 Clinical trial design ... 29
3.2 Model structure ... 30
3.2.1 Model assumptions ... 31
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3.2.2 Model perspective ... 32
3.2.3 Time horizon and discounting ... 32
3.2.4 Model outcomes ... 32
3.2.5 Cost-effectiveness threshold ... 33
3.2.6 The budget Impact ... 34
3.2.7 Dealing with uncertainty ... 34
3.2.8 Heterogeneity, variability & validity ... 35
3.3 Dataset ... 37
3.3.1 Epidemiologic data ... 37
3.3.3 Cost data ... 40
4 Results ... 44
4.1 Resource use ... 44
4.2 Budget impact ... 48
4.3 Cost-effectiveness ... 52
4.4 Uncertainty analysis ... 54
4.5 Probabilistic sensitivity analysis ... 54
4.5.1 CEAC & Expected value of perfect information ... 56
4.5.2 Expected value of perfect information for population ... 58
5 Discussion ... 60
5.1 Clinical aspects ... 62
5.2 Analytical aspects ... 63
5.3 Limitations ... 65
5.3.1 Clinical limitation ... 65
5.3.2 Analytical limitation ... 65
5.3.3 Limitation in economic evaluation ... 66
5.3.4 Limitation of costing ... 66
5.3.5 Limitation of decision tree model ... 67
6 Conclusion ... 68
Literatures ... 69
Technical appendix ... 76
TA.1 Cost components ... 76
TA.2 Decision tree model ... 79
TA.3 Additional results ... 81
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Abbreviations
ACIS Adenocarcinoma in situ
AGUS Atypical glandular cells of undetermined significance ASC-H Atypical squamous cells, cannot exclude a high-grade lesion ASC-US Atypical squamous cells of undetermined significance BIA Budget impact analysis
CC Cervical cancer
CEA Cost-effectiveness analysis
CEAC Cost-effectiveness acceptability curve CIN
CRN
Cervical intraepithelial neoplasia Cancer Registry of Norway CUA Cost-utility analysis
DRG Diagnosis Related Group EVPI
EVPPI
Expected value of perfect information
Expected value of partially perfect information HPV
HRQoL
Human papillomavirus Health-related quality of life
HSIL High-grade squamous intraepithelial lesion ICER
GP
Incremental cost-effectiveness ratio General practitioner
LBC Liquid based cytology
LSIL Low-grade squamous intraepithelial lesion
LY Life years
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NHS
Norwegian Cervical Cancer Screening Programme National healthcare system
NMB NPV
Net monetary benefit Negative predictive value PSA
PEVPI
Probabilistic sensitivity analysis
Population Expected value of perfect information QALY Quality adjusted life years
RCT Randomized controlled trial VOI Value of information
WTP λ
Willingness to pay Threshold
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List of tables
Table 1. Histology of cervical biopsy and classification of cytology ... 8
Table 2. The relative risk of developing cervical cancer and CIN2+ ... 11
Table 3. Summary of model types for analytic decision-making ... 19
Table 4. Epidemiologic data and test results ... 39
Table 5. Cost components ... 42
Table 6. Resource use over screened women ... 44
Table 7. Resource use over screened women in primary cytology screening ... 46
Table 8. Resource use over screened women in primary HPV screening ... 47
Table 9. The budget impact from screening tests, biopsies and conizations ... 48
Table 10. Total cost from screening tests in primary cytology screening ... 49
Table 11. Total cost from screening tests in primary HPV screening ... 50
Table 12. The expected cost, total cost, CIN2+ detected per screened woman and total CIN2+ cases ... 52
Table 13. The average cost, CIN2+ detected per screened woman and ICER ... 53
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List of tables
Technical appendix tables
TA. Table 1. Cost combinations for estimating unit costs. ... 76
TA. Table 2. Cost codes used for the cervical cancer screening program ... 77
TA. Table 3. Cost of invitational letters ... 78
TA. Table 4. Distribution of screening consultations in the screening program ... 78
TA. Table 5. 2. Scenario: Resource use over screened women ... 82
TA. Table 6. 2. Scenario: The budget impact from screening tests, biopsies and conizations. ... 83
TA. Table 7. 2. Scenario: The expected cost, total costs, CIN2+ per screened woman and total CIN 2+ cases ... 84
TA. Table 8. 2. Scenario: The average cost, CIN2+ detected per screened woman and ICER ... 84
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List of figures
Figure 1. The female reproductive system ... 5
Figure 2. The progression and regression of HPV ... 10
Figure 3. The incidence rate of cervical cancer from 1954-2013 per 100 000 in Norway ... 12
Figure 4. The incidence rate of cervical cancer from 1961-2009 per 100 000 in Norway, Sweden and Finland ... 13
Figure 5. The algorithm of primary cytology screening offered to women born on odd date from 25-69 years ... 14
Figure 6. The algorithm of primary HPV screening offered to women born on even dates from 34-69 years ... 15
Figure 7. The algorithm for women in follow-up ... 15
Figure 8. Formulation of the incremental cost-effectiveness ratio ... 18
Figure 9. Flowchart diagram of the budget impact ... 21
Figure 10. Illustration of the cost-effectiveness plane ... 24
Figure 11. Cost-effectiveness acceptability curves ... 25
Figure 12. Formulation of the expected value of perfect information ... 26
Figure 13. Formulation of the expected value of perfect information for population ... 27
Figure 14. Total cost through different screening rounds in unadjusted setting. ... 51
Figure 15. Total cost through different screening rounds in adjusted setting. ... 51
Figure 16. The cost-effectiveness plane in four settings ... 54
Figure 17. The cost-effectiveness plane in four setting from the PSA ... 55
Figure 18. The cost-effectiveness plane using incremental cost and effect from the PSA ... 55
Figure 19. The cost-effectiveness acceptability curve and EVPI in unadjusted setting ... 57
Figure 20. The cost-effectiveness acceptability curve and EVPI in adjusted setting ... 57
Figure 21. The expected value of perfect information for population ... 58
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List of figures
Technical appendix figures
TA. Figure 1. Decision tree model for women assigned to primary cytology screening in one screening round ... 79 TA. Figure 2. Decision tree model for women assigned to primary HPV screening in one screening round ... 80 TA. Figure 3. Total cost through screening rounds in unadjusted setting for BIA and CEA. 81 TA. Figure 4. Total cost through screening rounds in adjusted setting for BIA and CEA. .... 81 TA. Figure 5. 2. Scenario: The cost-effectiveness plane in four settings ... 85 TA. Figure 6. 2. Scenario: The cost-effectiveness plane in four settings from the PSA ... 85 TA. Figure 7. 2. Scenario: The cost-effectiveness plane using incremental cost and effect from the PSA ... 86 TA. Figure 8. 2. Scenario: The cost-effectiveness acceptability curve and EVPI in unadjusted setting ... 86 TA. Figure 9. 2. Scenario: The cost-effectiveness acceptability curve and EVPI in adjusted setting ... 87 TA. Figure 10. 2. Scenario: The expected value of perfect information for population in unadjusted and adjusted setting ... 87
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Terms
Biopsy – an examination of tissue removed to discover the presence, cause, or extent of a disease.
Carcinoma in situ – is a general term for an early stage cancer. Carcinoma in situ is also referred to as stage 0 cervical cancer. It is non-invasive, which means the cancerous cells are confined to the surface of the cervix and have not penetrated more deeply into the tissues.
Colposcopy – is a procedure for a visual examination of the cervix, vagina and vulva for signs of disease.
Conization – refers to an excision of a cone-shaped sample of tissue from the mucous membrane of the cervix. Conization may be used either for diagnostic purposes, as part of a biopsy or for therapeutic purposes to remove pre-cancerous cells.
Cytology – is the study of the appearance of cells for diagnosis of abnormalities and malignancies.
Diagnostic test – finds out if a disease is present, and if so, the test may classify the disease.
Histology – the study of the microscopic structure of tissues.
Herd immunity – occurs when a vaccinated population provides protection by reducing the spread of illness for individuals who are not resistant to a specific disease.
Incidence rate – is the number of new cases per population at risk in a given time period.
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Morbidity – is the proportion of illness for a specific disease in a geographical community.
Negative predictive value – is the probability of a patient testing negative, truly do not contain the disease.
Pap smear – a Pap smear, also called a Pap test, is a screening procedure for cervical cancer.
It tests for the presence of precancerous or cancerous cells on the cervix by collection of cell samples and spread on a glass slide.
Prevalence – is a measurement of all individuals affected by a disease at a particular time.
Routine screening – is a regular preventive testing over a period of time. In cervical cancer, women are typically recommended to return for routine screening between every 3 to 5 years.
Screening test – is used to find people who might have a certain disease before they develop symptoms. A screening test is expected to find nearly all people who are likely to have the disease or at high risk of developing the disease.
Sensitivity – is the ability of a test to correctly identify those with the disease.
Specificity – is the ability of the test to correctly identify those without the disease.
Triage (reflex test) – is to test for an optional test after the initial test has been pulled, by using the same sample of cells that were taken during the screening test. Triage is intervened between a screening and a diagnosis test to further stratify individuals with positive primary screening result.
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1
1 Introduction
Screening is still recognized as the cornerstone for preventing incidence and mortality against cervical cancer (CC). Among all malignant tumors, CC is the one that is most effectively managed by screening [1]. The most acknowledge method for detecting cervical precursor lesions against CC is managed through cytology screening [1]. This traditional screening method has been in use since the mid-19th century and still remains a common practice today [2]. However, in the last 20 years, the method has been widely debated due to its requirement for highly trained practitioners and laboratory equipment for sensitive test results [2]. Because of the inconvenient expenditure of organizing such program, low- and middle-income countries struggle for coverage of implementing such program, necessitating for alternative screening methods [2].
Due to better knowledge of the natural history of the disease, new technologies and screening solutions are developing in the field [1]. Persistent infection with specific oncogenic Human Papillomavirus (HPV) types are now accepted as a major factor for the development of CC [1].
This link has stimulated the establishment of adding HPV testing into the screening program.
HPV-test is highlighted as a more effective screening method because of its rapid approach for detecting HPV DNA and for requiring less human resources. However, implementing HPV testing is limited due to the need for expensive laboratory infrastructure which is primarily a related issue in developing countries [3].
A good screening depends on the quality of its test. A tests’ characteristic is determined by its diagnostic accuracy, in terms of sensitivity and specificity. The sensitivity of a test is defined as the ability to correctly identify those with the disease. On the other hand, a specificity of a test is defined as the ability to correctly identify those without the disease [4]. Sensitivity and specificity are defined as “inversely proportional”, which means that as the sensitivity increases, the specificity decreases, and opposite [4]. In CC screening, HPV-test is recognized as a more sensitive screening method for detecting precancerous lesions compared to cytology.
Evidence has shown that HPV-testing increases the percentage of positive test results for detecting cervical precursors. Thereby, an increase in follow-up testing and resource use in the first screening round(s) will follow. This is looked upon as a necessary process to prevent additional cases looking long-term because it opens the opportunity to offer better protection against high-grade cervical lesions in further screening intervals [5].
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A related discussion in the screening program for CC lately, has been the introduction of primary HPV screening. The new perspective has led to additional research to create evidence and convincement to the authorities for implementation. Around the world, there are many countries that are planning it, but have yet not fully implemented it. What is known to be true, is that it is easier to plan for implementation than it is to achieve implementation due to the many complicated issues that appear. Norway is one of the countries in Europe that is in the process of applying the screening method as a primary test. In spring 2015, the Norwegian Cervical Cancer Screening Programme (NCCSP) initiated the implementation of primary HPV screening to replace cytological analyzation in four countries as a quality assurance under randomized and controlled trial [5].
Aim of the study
Even though an observation of a new screening method detecting more precursor lesions than a conventional screening, does not adequately determine improved effectiveness [1]. Because of the frequent regression of precursor lesions, a measure of specificity is also compulsory to avoid anxiety, unneeded treatment and adverse effects [1].
In terms of coverage, protection and prevention, CC is best properly managed on a national level. When budget-holders are deciding on screen and treatment strategies, it is essential to consider the downstream consequences from test results such as incidence, mortality, side- effects from treatment and the resource use. Because of this concern, recommendations from economic evaluation about the sensitivity and specificity on screening tests and strategies, and also, the benefits and harms of treatment could be useful before any implementation towards a wider level is placed [2].
Throughout the years, multiple of economic evaluations have been conducted to distinguish the benefit of implementing primary HPV screening compared to primary cytology screening.
According to one cost-effectiveness analysis (CEA), current cytology-only screening in Norway was presented as costlier and less effective compared to strategies switching to primary HPV screening [6]. Another paper in Norway estimated that implementing HPV as a front-line strategy could lead to a cost savings of 200 million NOK annually, looking from a societal perspective. Half of these savings were referred as less travel costs and productivity loss, whereas the rest were presented from fewer healthcare services and medical costs [7].
3 Budget impact analysis (BIA) from Denmark concluded that primary HPV screening was able to improve detection of CIN2+ from 51.1% to 71.2%, leading to an incidence reduction from 188 to 150 cases. This led to an annual cost increase of 105 to 144 million DKK [8].
Even though CEA have indicated HPV testing at first line is a more cost-effective strategy, the feasibility of implementing it in Norway remains unknown. Therefore, this thesis undertakes to estimate the clinical and budgetary impacts of adopting HPV screening as primary based compared to the current screening program. The analysis will look at the short-term consequences and try to give a picture of what is expected, and more importantly, needed from a third-party payer perspective as well as looking at the cost-effectiveness comparing the two strategies.
In part 2, we will introduce the clinical background and explain how screening for CC works.
We will take part in the two strategies in Norway, and furthermore, cover the theoretical aspects of economic evaluation for this thesis.
In part 3, a detailed lineup of the model structure for the analysis will be explained, including model assumptions, model perspective, time horizon and model outcomes. We will also go through the material inputs and clarify how it was gathered and developed for the model.
In part 4, the results of our analysis will be presented on the basis of our model and structure.
Our outcomes will be informed in the setting of resource use, budget impact and cost- effectiveness. In addition, results from the uncertainty analysis will be displayed.
In part 5, an interpretation of our results will be clarified and discussed through several aspects.
Also, we will discuss the limitations in modeling and its implications for this analysis.
Finally, in part 6, a conclusion for the analysis will be drawn.
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2 Background
Clinical background
CC is a disease that occurs in the cervix and is an abnormal growth of cell that has the ability to invade the surrounding tissue. The cervix is located at the lower end of the uterus or womb (which is where the infant grows when the mother is carrying a child) and connects with the vagina [9]. This form of cancer is one of
the most frequent types of cancer among women globally, especially in undeveloped countries.
According to the GLOBOCAN project, CC is the fourth most prevalent type of cancer in women with an approximated estimate of 528 000 new cases (responsible for 12% of all female cancers) and 266 000 deaths (responsible for 7,5% of all female cancer deaths) globally in 2012 [10]. CC primarily influence younger women, with the majority occurring in the ages around 35 to 50, when women are involved in their careers and/or caring for their families [1].
Infection with HPV has been known to be the main cause to CC for several of decades now.
HPV is a sexually transmitted virus and is so common that about 75% of men and women who are sexually active, will be exposed to it at least once in their lifetime [9, 11]. In most cases, the virus will be taken care of by the immune system without any symptoms, but in roughly 10%
of infections, a persistent infection will develop, and the chances of developing premalignant lesions and CC will increase [12].
The risk of developing CC emerges in four steps: HPV transmission, growing persistence, a progression of infected cells to precancerous condition, and invasion [13]. Together approximate 99% of all CC cases are caused by HPV [14]. HPV is classified into low- and high- risk types. Low-risk types are associated with low-grade cell growth and are likely to regress without any treatment. However, an infection with a high-risk type can develop to cancer in a timeframe of 10 to 15 years, if left untreated [14]. Two types stand out and is involved in 70%
of all CC, HPV genotype 16 and 18. HPV16 is the most carcinogenic genotype and is responsible for 60% of all cases, while HPV18 reach in 10% of them [5].
Figure 1. The female reproductive system (1).
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2.1.1 Cervical cancer screening
Prevention for CC can be implemented at primary, secondary and tertiary level. Primary prevention is aimed to reduce risk factors by increasing individuals’ resistance to the disease before it occurs [15]. Vaccination against HPV is an example of primary prevention which reduces the likelihood of being infected by the virus. Vaccination is highly recommended as it benefits both from a clinical and economic perspective because of its positive externality. A positive externality is seen a social gain when a third-party is benefitting he or she did not pay or consume for [16]. In the vaccination context, a positive externality is present when herd immunity prevents spread of the disease in the community [17].
Secondary prevention concerns itself with detecting diseases in the preclinical phase. The motivation is to treat early and to reduce the chance of disease development. Screening is a type of secondary prevention and will be the focus of the thesis regarding CC. Finally, tertiary prevention focuses on the treatment phase of diseases in order to prevent complications or death [18].
(Wilson & Jungner, 1968:7) [19]:
“The object of screening for disease is to discover those among apparently well who are in fact suffering from disease. They can then be placed under treatment and, if the disease is communicable, steps can be taken to prevent them from being a danger to their neighbour”.
People go to screening because they wish to, in this case, avoid CC. If they however already have CC, they would like to be diagnosed early rather than late to prevent severe complications from treatment, or in worst case, die. With early detection, we are able to catch conditions that already have produced pathological changes, so we can hopefully cure them before it has the chance to spread [19]. Early detection aims, firstly, to control the communicable diseases, and secondly, to promote better health and reduce pain from disease. To achieve success of early detection, bringing the correct population to diagnosis and treatment, and also, avoiding harm to those that are not in need of treatment is important.
For this reason, World Health Organization guidelines devoted ten criteria “The principles of early disease detection” that should be met before implementation is placed [19]. These principles are essential when screening programs are carried out in public health assessments.
In the aspect of CC, the methodology relatively satisfies the proposed guideline, which indicates that prevention of the disease is applicable for screening [5].
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The principles of early disease detection
(Wilson & Jungner, 1968:26)[19]:
1) The condition sought should be an important health problem.
2) There should be an accepted treatment for patients with recognized disease.
3) Facilities for diagnosis and treatment should be available.
4) There should be a recognizable latent or early symptomatic stage.
5) There should be a suitable test or examination.
6) The test should be acceptable to the population.
7) The natural history of the condition, including development from latent to declared disease should be adequately understood.
8) There should be an agreed policy on whom to treat as patients.
9) The cost of case-finding (including diagnosis and treatment of patients diagnosed) should be economically balanced in relation to possible expenditure on medical care as a whole.
10) Case-finding should be a continuing process and not a “once and for all” project.
Ever since screening was established, the development of global CC screening program has gradually expanded. Even before the cause of the disease was known, screening for CC was already implemented. However, it was very much needed because of the growing morbidity [20]. HPV was suspected for a long time, but with over one hundred different genotypes existing, there was no significant evidence of the virus being the main cause. Until then the herpes virus was the prime suspect [20].
In the late 70’s, HPV was found as an agent in the abnormal cytological findings. One renowned man has taken more credit than others for his innovative work with the virus, Dr. Zur Hausen.
He and his colleagues were able to characterize HPV 16 and 18 and their correlation with CC [21]. This led to a huge turning point in the field with his research. The achievement raised the contribution of HPV vaccination programs and drove the continuation of screening strategies to new perspectives. In developed countries, CC is prevented through various degree because of extensive screening programs. The increased knowledge and emerging technologies continuously grow, and distinctive approaches have been implemented to reduce the burden of CC [3].
World Health Organization’s ten principles for public health assessments in case-finding.
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CC screening operates by detecting and treating high-grade cervical intraepithelial neoplasia (CIN), typically defined as CIN2+. CIN is pre-stages to cancer and classified into three levels, CIN1, CIN2, and CIN3. Histopathology gives the final diagnosis based on which treatment is planned. The diagnostic test is supplied for quality control of tissue on adequate samples and is provided by colposcopy-directed biopsy or conization examination [1].
A biopsy is used as the gold standard approach in indicating cancer, however, it is not preferred as it can be harmful to the cervix and cause discomfort for some women [22]. A screening test is more favored in the sense that it is easier to pull, causes less ache to the patient and is more affordable to perform [23]. HPV and cytology testing are these kinds of types, and studies tend to compare their relation to detecting CIN2+ [24].
Cytology testing
Women with abnormal cervical cell changes will either be diagnosed with low- or high-grade cytology. Table 1 represents the classification system of cytology used in Norway. Low-grade abnormalities are typically classified as ASC-US or LSIL and is corresponded as a grade 1 severity. High-grade abnormalities are classified as ASC-H or HSIL and corresponded as grade 2 or 3 severity. Other common types that are known are AGUS (atypical glandular cells of undetermined significance) and ACIS (adenocarcinoma in situ) [25].
Table 1. Histology of cervical biopsy and classification of cytology (2).
Abbreviations: CIN: Cervical intraepithelial neoplasia grade 1, 2 and 3.
A lesion is any abnormal change in the tissue, while dysplasia is any change of tissue.
Grade 1 is correlated as low-grade lesion or CIN1, while grade 2 and 3 is respectively correlated as CIN2 and CIN3 or high-grade lesion.
9 Abnormal cell changes from cytology are detected from samples of Pap smears and analysed by microscopic examination [26]. This subjective test is solidly based on human judgment, which makes the stability of the accuracy varied across countries because of the mixed level of healthcare infrastructure, especially between developing and developed countries [3].
A positive property of cytology testing is related to its high specificity, which is often applied around 91-98% for detecting CIN2+ [5]. Due to this quality, we are able to correctly identify patients without disease and clinicians are more convinced to exclude women from requiring follow-up. However, the test is restricted to not being very sensitive, often applied around 70%
for detecting CIN2+ [27]. This corresponds to an excessive number of patients testing false negative which applies that women could be requested routine screening even though the risk of developing CC is still existence. Prevention would then appear at a later stage than necessary [28].
Today, liquid-based cytology (LBC) test is a common method for sampling and is considered to be better than the Pap smears. LBC testing has the advantage to allow laboratories testing for HPV from the same samples [29]. If that is to test for HPV primarily or to reflex test conditional on abnormal cytology is depended on the outlines from the screening strategy. LBC is known for being more sensitive in the sense that it is easier and quicker to observe the cells [26].
Nevertheless, the importance that samples are being collected from the correct area still applies, and due to the dependency on the sampler, it is a common reason why the quality of the test is various [14, 27].
HPV testing
HPV-test is an objective molecular test in the sense that samples are classified through machines and is a widely encouraged method because of its practicality [14]. Ever since HPV was evidently known as the main cause for CC, HPV testing has been matched and combined with cytology for detecting CIN2+. The sensitivity for HPV-test is often applied between 90-96%
for detecting CIN2+ [26]. With this quality, the test is able to identify a greater number of patients who truly are exposed to the disease. A disadvantage of the method is due to the low specificity for CIN2+ (60-80%) [26]. This limit implies that for patients who are not carrying CIN2+, more women would test positive for HPV than they would of for cytology. This would also result in a higher number of patients being sent to follow-up even though they are not carrying carcinogens. This consequence is overtreatment, harmful to the patient and causes unnecessary use of time and resources.
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In 2014, the Food and Drug Administration approved the use of Cobas HPV-test as part of the primary screening program for CC [24]. Also, the abstract of current guidelines for managing CC has lately been expanded to include complete detailed instructions [1]. Many further recommendations on managing diagnosis and follow-up of positive precursors have been added. This entailed the inclusion of a separate chapter on a guided direction in handling with cytological abnormalities and histological CIN [1].
There are several reasons that implementation of primary HPV screening is preferred compared to cytology. First, since HPV is known as the cause for CC, it is reasonable to give the opportunity to detect the virus as early as possible. Being able to detect the genotypes earlier, we allow clinicians to properly take care of patients who are at an elevated risk of developing cancer [24]. If we are able to detect unwanted outcomes sooner, we also reduce the necessity of spending on further resources. Not only could there be cost savings, but when precursors are detected early, it is highly curable which proceed to a good quality of life and enduring survival.
Figure 2. The progression and regression of HPV (3).
The figure above represents the progression of different health states going from HPV negative till death, while the figure below shows the regression in CC.
Abbreviation: HPV = Human Papillomavirus, CIN = Cervical intraepithelial neoplasia, ICC = Invasive cervical cancer.
11 Secondly, as HPV-test is reckoned to be more sensitive, a higher number of cases will be detected. This would imply that more women are sent to follow-up and treatment, and for that, an increase in cost and resource use will follow in the short-term. However, because these women are treated at an earlier stage, a healthier screening population is expected to appear at the next intervals. Therefore, the high resource use will be traded off in the long run [5].
Thirdly, with an implementation of primary HPV testing into the screening program, we are able to extend the screening interval for the patients testing negative. A negatively infected woman has a low risk of developing CC in the next 5-10 years [26]. Another quality of the HPV-test is that the negative predictive value (NPV) is also consistently higher than cytology, which permits a longer screening interval [27]. NPV is defined as the likelihood of a patient who tests negative, truly do not contain the disease [4]. The high NPV of the HPV-test is yield by the high sensitivity rate. Stretching the interval would reduce tests taken and give doctors opportunity to see other patients rather than the same patient repeatedly.
Table 2 presents the relative risk of developing CIN2+ and cancer. To prevent the number of cancer cases efficiently, we would like to detect the characteristics that contain the highest chance for evolving invasive cancer.
None tests are 100% sensitive, and therefore, providers have a responsibility that women with high severity for staging CIN2+ (above 2%) should not be sent to routine screening [27]. Comparing the two screening tests, an individual with a result of HPV positive is likely to be at greater danger for developing high-grade precursors compared to a positive cytology.
Table 2. The relative risk of developing cervical cancer and CIN2+ (4).
Abbreviations: CIN = Cervical intraepithelial lesion, ASC-US = Atypical squamous cells of unsure determination, LSIL = Low-grade squamous intraepithelial lesion,
ASC-H = Atypical squamous cells where high-grade change cannot be excluded, HSIL = High-grade squamous
intraepithelial lesion, HPV = Human Papillomavirus.
12
2.1.2 Cervix cancer in Norway
Current prevention
In Norway, over 300 cases are diagnosed annually and around 80 women die of the disease every year [30]. The screening program today is integrated as part of the national healthcare system (NHS) and have been under national management with the first invitational letters for testing given in 1995. The current screening program in Norway is cytology-based with a three- year screening interval offered to women between 25-69 years. In addition, the prevention is offered through vaccination program for girls aged at 12 years [31] and there is also a catch-up vaccination program offered to females up to the age of 26 [32]. The vaccine should protect the HPV types that cause 70% of CC [31]. Because of the ongoing vaccination program, the majority of women in Norway will eventually consist of a vaccinated population. This will have an impact on the screening program in the future.
Figure 3 illustrates the history of incidence rates in Norway. From 1975, the incidence declined massively due to the high attention of cancer screening at that time. But in the early 90’s it seemed to have lost its interest, resulting in a steady increase before the implementation took place [33]. When the screening program was rolled out, NCCSP’s aim was to reduce the incidence and mortality rate by 50% [33]. According to the prediction of models, without no screening program in Norway, the estimated mortality could result in a number of 260 death yearly. With the national screening program, we are able to avert over 70% of CC deaths [5].
Figure 3. The incidence rate of cervical cancer from 1954 – 2013 per 100 000 in Norway (5).
13 Yet, women who are present for screening today, are most likely patients from an unvaccinated population. This reason for the incidence and mortality that still occur around the country and why the decline rate maybe is not steeper than intended. Comparing the figures to our neighbouring countries, like Sweden and Finland, the incidence in Norway is still above (Figure 4). It may be due to the fact that Finland and Sweden already had organised national screening program in the 60’s [33]. Norway was rather late in processing the screening program.
Another related factor may be due to the lack of coverage for screening in Norway. According to one paper, the cover in Norway was 67% after three years, and 75% after five years with the organised screening around the country [27]. More than half of the CC cases that appears is raised from the group of women who did not participate in regular screening [5]. This issue is not only a problem in Norway, but also a challenge in national screening programs in general.
Future prevention
Ever since the Cobas HPV-test was approved in screening for CC, Norwegian authorities decided to administrate the strategy of the current screening program. The Norwegian Directorate of Health (Helsedirektoratet) together with the support from the Norwegian Ministry of Health and Care Services (Helse- og omsorgsdepartementet) initiated the request for primary HPV testing to be implemented in Norway [34]. However, before any rollouts towards a national implementation should be considered, a pilot project should be crafted and demonstrated with appropriate results with respect to effectiveness, cost-effectiveness and until organizational tasks have been adequately determined [1].
Figure 4. The incidence rate of cervical cancer from 1961 - 2009 per 100 000 in Norway, Sweden and Finland (6).
14
Figure 5. The algorithm of primary cytology screening offered to women born on odd dates from 25-69 years (2).
Abbreviations: ASC-US = Atypical squamous cells of unsure determination, ASC-H = Atypical squamous cells where high-grade change cannot be excluded, LSIL = Low-grade squamous intraepithelial lesion,
HSIL = High-grade squamous intraepithelial lesion, HPV = Human Papillomavirus.
The feasibility and quality assurances should be validated, and the required training and administration should be organised. A quality assurance of a screening program requires a robust structure of program management and monitoring, making sure that all aspects of the project are performing sufficient [1]. If performed well, a transformation of the intervention has the possibility to improve the accessibility, the effectiveness, and the cost-effectiveness. The quality assurance should include randomised screening policy, with access to a quality- controlled regulation of a new strategy [1].
Today, an ongoing pilot project has been implemented in four counties in Norway (Rogaland, Hordaland, Sør- and Nord-Trøndelag) as a quality assurance as part of the NCCSP [35]. The recommended intervention offers HPV primary screening to women between 34-69 years with a five-year screening interval. The introduction of primary HPV screening aims to further reduce the incidence of treatment-enhancing precursors of CC, and also, to distinguish if the restructure is able to be at least as effective as the current strategy, if not better [5]. The screening algorithms from the pilot project are described as follows.
Primary cytology screening – todays national strategy
Women that are invited to primary cytology screening will be recommended to return to a new test in 3 years if their test result is normal. However, women with a test result of a low-grade cytology (ASC-US / LSIL) will be triaged (reflex tested) with HPV-test. Those who test HPV negative are recommended routine screening in 3 years, while women who test HPV positive will be advised new cytology and HPV-test in 6-12 months. Finally, women with high-grade test result (ASC-H / HSIL or more severe) will be suggested colposcopy examination with cervical biopsy according to the guidelines. This case will also follow with a test result of LSIL / more severe, or ASC-US / normal followed by an HPV positive from the reflex test.
Cytology
ASC-H / HSIL / >
Colposcopy w/biopsy
ASC-US / LSIL Reflex test
HPV positive HPV & cytology 6-12 mth
LSIL / > Colposcopy w/biopsy
ASC-US / Normal
HPV positive Colposcopy w/biopsy
HPV negative
Routine screening by
3 years HPV negative
Routine screening by
3 years
Normal Routine
screening by 3 years
15
Figure 6. The algorithm of primary HPV screening offered to women born on even dates from 34-69 years (7).
Abbreviations: ASC-US = Atypical squamous cells where high- grade change cannot be excluded, HPV = Human Papillomavirus.
Figure 7 The algorithm for women in follow-up (2).
Abbreviations: ASC-US = Atypical squamous cells of unsure determination, LSIL = Low-grade squamous intraepithelial lesion,
HPV = Human Papillomavirus, CIN = Cervical intraepithelial neoplasia.
Primary HPV screening – the recommended intervention
Women that are invited to primary HPV testing will be recommended to routine screening in 5 years if they are negatively infected. On the other hand, women with an HPV positive test result are triaged for cytology. A reflex test result of ASC-US or more severe, will indicate cell growth and thereby colposcopy with biopsy is recommended. However, women who test normal cytology in the reflex test will be suggested new HPV test in 12 months. If they test HPV positive again, a colposcopy with biopsy will be suggested. On the other hand, if they test HPV negative, routine screening will be requested.
Follow-up
Women who are sent to follow-up will be sent to colposcopy and cervical biopsy. With a diagnosis of CIN1 or normality, a new cytology and HPV-test will be suggested. However, if the tissue from the biopsy show any indication of CIN2 or more severe, a diagnostic conization will be recommended according to the guidelines, also known as a cone biopsy. Of the women that come for new testing, a second colposcopy with biopsy will be requested for the ones testing HPV positive regardless of cytology, or if the cytology says LSIL or more severe. Those who test HPV negative and normal will be requested a routine screening (the duration depends on which strategy the patient initially started on). Finally, a result of HPV negative with ASC- US implies potential of still bearing the risk of developing cancer. Those in this category are so far into the screening algorithm that it would be unsafe to recommend routine screening.
Retesting will most likely be requested.
HPV
HPV positive Reflex test
ASC-US / > Colposcopy w/biopsy
Normal New HPV 12 mth
HPV positive Colposcopy w/biopsy HPV negative Routine screening
by 5 years
HPV negative Routine screening by 5 years
Colposcopy w/biopsy
CIN2+ Conization
CIN1 / Normal HPV & cytology 6-12 mth
LSIL / > 2. Colposcopy
w/biopsy
ASC-US
HPV positive 2. Colposcopy
w/biopsy HPV negative New HPV &
cytology 12 mth
Normal
HPV positive 2. Colposcopy
w/biopsy
HPV negative Routine
screening
16
Unsatisfactory results
In the screening program in Norway, approximately 5% are experiencing unsatisfactory test results in cytology testing. If the laboratory is checking that an insufficient number of cells or too much blood in the samples are present, the test will be characterized as unsatisfactory.
In HPV testing, the unsatisfactory result is more unlikely because of samples are sent through machines for investigation. However, there are still possibilities that tests are not coming to a conclusion [27]. In HPV testing, unsatisfactory results are less than 0.5%. These approximations are based on observed data. Women experiencing this will be required to come back for retesting within 1-3 months. This additional testing and analyses will accumulate an impact on the costs.
17
Theoretical background
2.2.1 Criteria for prioritization
Demand for healthcare services is substantially increasing with time, and given the limited resources available, healthcare decision-makers face confrontation with allocation problems.
The demand for services is way over than what the healthcare sector is able to supply, leading to long waiting lists. Since resources are scarce and there are budgets that need to be met, making decisions for spending resource on something, will be less for something else [36].
Because of this limit, prioritization is a necessary approach to move forward on who should be treated. By prioritizing, one chooses someone or something in favour of one another. It is looked upon as unethical to not prioritize, so by choosing measures where it is most needed, we make the most of what is possible. At a public level, prioritization is based on authorized criteria and principals [36].
Due to the taxation system, all health services are funded by the public as part of the national budget, which means that all services are basically free to all resident in Norway [36]. The Norwegian NHS is built on equity. This implies that the principal of fairness and equal access to healthcare services is highly valued. Independence of residence, income, ethnicity, sex or social status should not prevent anyone from consuming health services [36]. When consumers pay nothing or a very small amount of an actual treatment, the demand for services will be high.
And when the public intends to not set up the costs, political decisions must be made of which patient groups should be prioritized in treatment [36].
In Norway there have been published three reports for priority setting for public health service;
Lønning I selection (1987), Lønning II selection (1997) and the Nordheim commission (2014).
Their objective was to highlight the action behind priority in the healthcare sector and establish criteria and guidelines. Five dimensions of priority were requested to be considered: 1) severity of disease, 2) equal options for treatment, 3) waiting lists, 4) health economic aspects and 5) patient’s social adherence [36, 37]. Today, guidelines for health economic evaluations in Norway base their recommendations on the setting from Lønning II and is considered as the central basis of priority setting in healthcare. The core values are based on three criteria; the severity of the condition, the expected benefit of service, and, the cost-effectiveness [36].
18
2.2.2 Economic evaluation
In a decision-making process, there is always a likelihood of making the wrong decision, resulting in not picking the most cost-effective intervention. Because budget holders intend to not “waste” money in the sense that they could have chosen other optimal options, the level of uncertainty plays a central role in this context [38]. The money “wasted” is in economic theory defined as the “opportunity cost”. To overcome this concern, decision-makers tend to rely on economic evaluations as it can provide evidence for which healthcare intervention to prioritize [39]. Economic evaluation is about determining whether the society’s use of resources of an intervention can be an effective policy in terms of both their costs and consequences [40]. The burden of an economic evaluation comprises the identity, measure of cost and health effect, valuation and comparison of the choices being considered [39].
2.2.3 Cost-effectiveness Analysis
CEA is an economic evaluation that assesses the health effects and costs of interventions and is designed to improve and strengthen the decision-making process regarding resource allocation [39]. CEA is applicable to single out the most cost-effective alternative where the health outcome is not measured in monetary terms [39]. Some people are distorted between CEA and cost-utility analysis (CUA) in the sense that CUA is a variant of a CEA. The main difference between them is that a CEA quantifies health outcomes in terms of measures of natural units such as life-years (LY) gained, while CUA uses measure related from health- related quality of life (HRQoL), in form of quality-adjusted life years (QALY) [40, 41]. There may be several reasons for the use of LY instead of QALY. Firstly, it could attach to the actual disease. Secondly, the measuring of the type of intervention might be more interesting corresponding to the type of technology [41].
With the incremental cost and effectiveness of our interventions, we are able to obtain the incremental cost-effectiveness ratio (ICER) based on our outcomes. The ICER is defined as:
((𝐸𝑋𝑃 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑖𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝐴) − (𝐸𝑋𝑃 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑖𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝐵))
((𝐸𝑋𝑃 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑖𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝐴) − (𝐸𝑋𝑃 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑓 𝑖𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝐵))= 𝛥EXP cost 𝛥EXP effect
= 𝑰𝒏𝒄𝒓𝒆𝒎𝒆𝒏𝒕𝒂𝒍𝒄𝒐𝒔𝒕 𝒆𝒇𝒇𝒆𝒄𝒕𝒊𝒆𝒗𝒏𝒆𝒔𝒔 𝒓𝒂𝒕𝒊𝒐 (𝐈𝐂𝐄𝐑)
, given that the intervention A is the costliest.
Figure 8. Formulation of the incremental cost-effectiveness ratio (8)
19 To obtain the maximum ICER of the intervention, we require some information that the value is dependable and can be considered cost-effective, by comparing it to the cost-effectiveness threshold (λ). The threshold, also known as the willingness to pay (WTP), can depend on many factors. Individuals’ health benefits and attitude towards risk are clear examples in the sense of public health that has to be considered as well as the budget available [40]. If the ICER is lower than the cost-effectiveness threshold, then standard decision rule would conclude that the intervention is considered as cost-effective. We would then choose the option with the highest ICER as long as it meets up to the budget holders WTP [40].
2.2.4 Decision analytic modeling
In the context of economic evaluation, decision analysis has been characterized as a standard access to decision-making under uncertainty. Decision-analytic models are algorithmic designs that are used to resemble the health effects to an individual or a population under a number sequence of events [42]. They are used to project a set of possible consequences that progresses from a set of alternatives that are classified. Decision models can be an effective tool for evaluating healthcare policies and for improving the quality of public health and decision- making [42].
Models can be used to 1) undertake beyond time horizons of interventions, 2) conclude to other subgroup populations that are not observed directly in the study, 3) incorporate inputs from multiple sources, 4) evaluate appropriate comparative interventions that have not been included in studies, and 5) engage in intermediate outcome measures (e.g., cases of cancer) [42]. The approaches to decision analysis add orderly and specific way to examine decision-making.
Table 3. Summary of model types for analytic decision-making (9).
20
There are several types of model types to choose from in economic evaluations. Which one to derive from depends particularly on the assessment in place [42]. Table 3 gives a summary of the fundamentals of the model types and in which case they are adapted for.
Markov models are ideal in modeling for clinical problems that involve risk over a lifetime, or when the timing of events is important. The advantage of Markov model is that it captures many aspects that are present in a clinical process. The disadvantage is that the underlying assumption of the probability of moving from one health state to another is purely based on the current health state that is given and not on past events [42].
Microsimulation models, known as individual sampling modeling, process diseases by simulating one individual at a time which allows the model to keep track history for everyone.
The disadvantages are that the simulation takes a considerable amount of computer time [42].
Dynamic models are used in case of communicable diseases and simulate how interactions of disease spread over time and can be a logical choice for vaccination against herd immunity [42].
Discrete event simulation models assess the progress of systems and are common in patient scheduling and flow schemes by using a distribution of time for events instead of probabilities like the Markov model [42].
Decision tree models add logical structure to the possible consequences and give decisions for the events occurring. These consequences are expressed in terms of probabilities to describe the likelihood of an event. On the basis of the inputs in modeling, each probability has a cost and an outcome. This allows us to calculate the expected cost and expected outcome of each consequence in the model [43]. A statement applies that since the expected utility theory is a normative framework for decision-making under uncertainty, then using expected value for decision models should provide key outputs for decision-making as long as the inputs have been chosen appropriately and provided a clear indication of the preferred interventions being compared [44]. A clear advantage of this type of model is that they are created in a consistent and easy-to-follow way. Observers can easily read from each event and follow the pathway probability as well as decision options [40]. Because the probabilities are related to those patients who have previously experienced a particular condition, the model is able to show individuals possible prognoses when following a type of intervention [44]. Decision trees allow us not only to consider the risk of the possible consequences, but also to arrange the appropriate action to outcomes at every event [45]. The disadvantage is that the model is only applicable in events of limited recurring events and in short time horizon [42].
21
Figure 9. Flowchart diagram of the budget Impact (10).
2.2.5 Budget Impact Analysis
BIA is also a concept underpinning economic evaluation. In healthcare setting, the purpose to perform BIA is often, to support alongside CEA/CUA, as an application for the national reimbursement [46]. While CEA/CUA helps to assign priority to interventions by measuring the value in terms of cost per QALY or other health effects such as LY gained to estimate their cost-efficiency, a BIA provides useful information to tackle the affordability of an intervention for the stakeholder [46]. BIA is most valuable when an intervention is being translated from research into practice. The method is adopted to evaluate the expected short-term alterations in expenditures for a healthcare intervention [47].
Performing such analysis requires detailed estimation of costs associated with changes in staffing and use of technology, in a number of patients receiving new intervention and also, in savings. The end result should determine the total expenditure associated with adopting a new intervention [47]. When modeling, the validation should minimum carry face validity alongside decision makers and calculations verified. Data inputs for the BIA should contain published estimates for comparative analysis due to the efficiency to support current and new intervention as well as the target population [47].The target population in the analysis should include all eligible patients, that is, the incidence and the prevalence of the interventions during the time horizon of interest. If testing is used to identify individual’s risk, this should be presented, and the frequency of use should reflect real practice [47].
22
An introduction to new interventions could lead to changes in symptoms, in health outcomes and in progression due to the disease, and thus, in changes of the consumption for condition-related healthcare services. In those cases, if data are feasible and these differences have an impact on resource use, condition-related costs should be displayed in the study [47].
When we are able to obtain the key factors in the different interventions and put them into numbers, the “cost of illness” will be the end result of those factors. The differences of the “cost of illness”
from the current with the new intervention will result in the budget impact. Figure 9 illustrates the main components of a BIA [47].
2.2.6 Micro- & case-mix costing
From an economic point of view, costs are looked upon as the sacrifice of benefits for one another when consuming a resource. When calculating costs, we can think of two elements; (1) the number of resources (or activities) used in a given study and, (2) the unit price of that activity [44]. It is worth to take into account the level of precision for the estimations when costing. This is an important decision to be made because, as the total cost is recognized as the unit prices multiplied with the resources used, the more accurate the prices are, the more certain are we of our result [44].
Two types of costs are conducted within the healthcare sector; the ones that appear by the use of an item and the ones that are because of this use [44]. In screening cancer, we include costs that are correlated with the screening itself. Typically, we would think of the actual procedure and the physician time, but also, further tests and follow-up and the consequences that are linked with screening would need to be considered. This type of costs is defined as direct medical costs and are related from a healthcare perspective.
Several cost components are excluded from the healthcare sector. These costs typically include labour and leisure costs, referred as indirect costs. Transportation costs are also a measure of matter and are linked as a direct non-medical cost [44]. These costs are not only related to the patient, but also the people surrounded by that patient and are expenditures that should be taken into account from a societal perspective.
In micro-costing, every component of resource use is estimated in terms of unit cost. The unit price would take into account components as staff time, facilities, equipment used, sending samples, laboratory testing, and information. This form of costing is considered as the most
