Cervical Cancer Screening: Cost-Utility Analysis And Sensitivity Essay.

Cost Allocation for Part Four

Cervical screening tests aim to identify the prevalence of precancerous stages within a women's body. Pap smear test is one of the most effective cervical screening tests that are followed by clinicians to examine the occurrence of the cancerous stage. Cervical neoplasia could be easily detected with the help of these screening tests thereby preventing the affected women from developing cancer. A cohort study has been carried out, and test results will be interpreted in this current report. Based on the clinical test results, an analysis will be carried out, and finally, a recommendation will also be provided that will help any women to understand the occurrence of any cervical cancer.

In a conventional Pap smear test, the clinician collects cells from the cervix of the presumed affected patient and smears them on a microscopic slide to test them. Adherence to these cells is carried out with the help of fixative. These slides are then sent to laboratories for evaluation. In the test, sensitivity and specificity examination of the smeared cervical cells are carried out to understand their characteristics.

In the first clinical study, the clinicians observed sensitivity to be 74% while specificity was about 94%. The high sensitivity of the test denotes that this test was able to identify the total number of patients without any disease (Rho Cervical Cancer, 2018). However, the clinician was not satisfied with the test results and carried out a new examination of the similar cervical cells.  

New Test	Disease Status		Total
	Cervical cancer (+ve)	Cervical cancer (-ve)
Test (Positive)	49	36	85
Test (Negative)	2	564	566
Total	51	600	651

Table 1: New Pap smear test

(Source: Created by the researcher)

True positive- In medical testing, true positive results denote that these tests significantly depict true results regarding the presence of any disease. Canfell et al. (2017:100) commented that true positive tests enable any clinician to identify the presence of any disease. The value of this test is- 49

False positive- In any case, the clinician has carried out a negative test on the affected patients to test the prevalence of any disease. However, in this case, the patients showed positive results. The value of this test is - 2

True negative- In this medical test, the patients showed negative results. This suggests that the presumed patients did not suffer from cervical cancer and they do not have any cancerous growth in their cervical part. The value of this test is- 564.

False negative- This test results wrongly interpret regarding the prevalence of any health condition. Therefore, it can be assumed by the clinician that the affected patients did not suffer from cervical cancer. The test value of this test is- 36.  

True positive (a) 49	False positive (c) 2
False negative (b) 36	True negative (d) 564

Cost-Utility Analysis

Table 2: Sensitivity and specificity calculation

(Source: Created by the researcher)

Sensitivity= a/ (a+b) = 57.65%   ≈95% Cl

Specificity= d/(c+d) =99.65%   ≈95% Cl

Accuracy rate= (a+d)/ (a+b+c+d) = 94.16%     ≈95% Cl

This new cohort study significantly shows a better and higher specificity test as compared with the previous one. Blatt et al. (2015:282-288) commented that higher (99%) specificity test means successful identification of those patients without any disease. This specificity test was able to analyze the total number of patients that did not suffer from cervical cancer. Therefore, proper analyzation of the number of patients without having any disease is beneficial for these kinds of tests.

However, in this new test, the sensitivity rate is 57.67% which lower than the initial test (74%). Therefore, lower sensitivity test depicts the inability to identify the total number of patients suffering from cervical cancer. Overall high specificity test is beneficial for any clinician in analyzing the total number of patients suffering from cervical cancer.    

Decision trees help to support a deduction that is to be taken after a set of observation in a given study. In this report, the applicability of the tests is determined by the help of a decision tree to evaluate the efficiency of the tests in consideration with possible consequences. The said consequences involve outcomes from chance events, utility and resource costs. Malanga & Mautner (2016: 139) consider decision trees display specific algorithms containing condition-controlled statements.

The main elements of a decision tree are given below, with an appropriate explanation to justify the terminologies.

Root node: Root node of a decision tree represents a batch or sample of the population that is further segregated into additional homogeneous datasets.

Splitting: Splitting refers to the process divide a node in the decision tree into multiple sub-nodes (Arrossi et al. 2015: 85-94). Here, splitting has been done on cancer and no-cancer nodes.

Decision node: A decision node is created when a sub-node is divided into further intermittent nodes to reach a decision. In this case, the decision nodes are presented through outcomes about costings and relevant benefits.

Terminal node: Ferri (2017: 199) states the nodes that do not undergo splits are termed as terminal or leaf nodes. The nodes that defined the nature of the outcomes are thereby considered as terminal nodes.

Pruning: Pruning we remove sub-nodes of a decision node, this process is called pruning. You can say the opposite process of splitting.

Sensitivity Analysis

Branch: Branching of a decision tree provides an entire subsection of the tree for further determination of the values. The present decision tree has undergone several branches to evaluate the efficiency of the tissues.

Parent and child nodes: Parent and child nodes are segregated by their inputs and outputs. The main node, from where the sub-nodes appear is the parent node, and the said sub-nodes are called child nodes. For instance, a cancer node (parent) is divided into false positive and true negative nodes (child nodes).

Figure 2.3.1: Decision tree (unpopulated)

(Source: Given by Researcher)

Crowding in oncology departments (OD) create considerable adverse consequences for the patients. The routine accumulations of accuracy tests are compared through algorithms to predict admission risks in OD. The algorithms used here has built a predictive model of the decision tree to show that the new test exhibit better results with (specificity = 99.65%, sensitivity = 57.65% and accuracy = 94.16%) than the previous tests. The FOBT sensitivity and specificity involve 0.805 and 0.708 respectively. Drawing on this logistic model, several parameters are deduced to influence patient flow, like the rate of admissions, arrival mode, care groups, and previous admission.

Quality-adjusted life year (QALY) is used to measure disease burden that includes both the quality as well as quantity o the total numbers of lives lived by the affected patients. Campos et al. (2015:2208-2219) commented that QALY is an economic evaluation of assessing medical interventions that are required for the patients. Canfell et al. (2018:16700) opined that QALY could be effectively used by clinicians to  

True positive: √0.91²x 40² = 0.8281+1600= 40.01

False positive: √0.89²x 35²= 0.7921+1225= 35.01

True negative: √0.91²x 40 ²= 0.8281+1600= 40.01

False negative:  √0.45²x 40²= 0.2025+1600= 40.00

1. b) The costs and benefits of economic model are mentioned below-

Individuals	Cost	Benefit
True positive	$22,400	$0.48
False negative	$32,200	0.42
False positive	$350	0.83
True Negative	$50	0.90

Table 3: Cost and benefit table

(Source: Created by the researcher)

Discounting future costs is beneficial for obtaining effective test results. Demarco et al. (2018:1910) commented that costs and benefits are required to be discounted to minimize the high prevalence of health issues. Hariri et al. (2015:1608-1613) further added that costs and benefits discounting might penalize preventative health care programs. Thus, it is essential for the clinicians to analyze effectively the health outcomes of their patients to minimize their future costs.  

To obtain effective results, new pap smear test required to be performed by the clinicians to understand and identify the patients suffering from a cervical test. Appointment of the general physician is also required by the patients in order to understand the test results. Sabatino et al. (2015:464-468) opined that treatment is not required for true negative patients. Simms et al. (2017:366) commented that cervical pap smear test is used to analyze the occurrence or prevalence of cancerous cells in the cervix of the presumed affected patients. Similarly, early treatment is required for true positive patients. Crowe et al. (2014:1455) commented that true positive patients seem to suffer from precancerous growth and therefore, proper and early treatment is required to secure their health condition. False negative patients should be provided with proper and appropriate treatment. However, in case they do not follow early treatment options, then delayed treatment should be provided to them.

1. b) Estimation of cost elements for the current test

		Current test	New Test	GP visit	Further exam – no treat	Further exam – early treat	Delayed treatment	Total
Current test	True Positive	$50		$70		$1000		$1120
	False positive	$50		$70	$500			$620
	True negative	$50		$35				$85
	False negative	$50		$35			$50,000	$50,085

Decision Tree Analysis

Table 4: Current cost element test

(Source: Created by the researcher)

		Current test	New Test	GP visit	Further exam – no treat	Further exam – early treat	Delayed treatment	Total
New Test	True Positive		$400	$70		$1000		$1470
	False positive		$400	$70	$500			$970
	True negative		$400	$35				$435
	False negative		$400	$35			$50,000	$50,435

Table 5: New cost element test

(Source: Created by the researcher)

Cost-utility analysis5A Model parameters

Parameter description	Current Test	New Test
Prevalence of cervical cancer	0.001	0.001
Specificity of test	0.94	0.99
Sensitivity of test	0.74	0.57
Cost – True Positive	$1120	$1470
Cost – True Negative	$85	$435
Cost – False Positive	$620	$970
Cost – False negative	$50,085	$50,435
QALYs – True Positive	0.48	40.01
QALYs – True Negative	0.42	40.01
QALYs – False Positive	0.83	35.01
QALYs – False negative	0.92	40.00

Table 5.1: Model parameters

(Source: Given by Researcher)

Figure 5.1: Decision tree with cost effectiveness

(Source: Given by Researcher)

The given decision tree is driven by the constructed algorithms through data mining in the statistical package. For instance, the dialogue boxes used in this decision tree incorporates the four algorithms about the nature of outcomes. The parameters allocated in this area involve minimum costs of screened and unscreened population. Also, the QALY benefits in splitting nodes to the root node. Stopping parameters are selected by cost-effectiveness and of both previous and new tests.

The expected costs of each parameter are determined by QALY appraisals and IECR scores. Costs for true positives pertain to the people who are screened and are found positive for cancers confirmed to $1120 as per the previous test. However, the new test has defined this cost to be $1470 due to increased costs of resources. The cohort which has been screened for cancer and found to be clean has to pay $85 and $435 respectively. This is because new tests utilize advanced resources for cancer screening. The false positive result costs rise from $620 to $970 due to the application of advanced tests, and double visits to the physician. Costs due to false negativity have suffered a minor increment, from $50,085 to $50,435. The excessive charges depend on the delay to obtain relevant treatment.

Outcomes	Cost	Benefit
True positive	$1,470	40.01
False negative	$970	35.01
False positive	$435	40.01
True Negative	$50,435	40
Cost of new test	$400
		inc cost	$97.72
		inc benefit	0.0236929
		ICER	$907.28

Table 5.3: ICER determination

(Source: Given by Researcher)

Figure 5.1: Decision tree with ICER

(Source: Given by Researcher)

The costs of the services are tallied for each outcome, for both previous and new tests. The decision maker, as per the strategisation of the decision has set up the mark at the explicit threshold limit of $50,000 for each unit in QALY. Thus, it can be stated that the new test is cost-effective in consideration to the parameters involved.

Variations in parameters, like costs and its benefits while considering the QALY of the scenario is considered to have minimal influence on ICER for the previous test. Similarly, cost and benefits acted as significant components of the new test in case of the high-risk female population. Minimal influence on ICER creates a negligible effect on the overall finding of the cost?effectiveness.

Comparison of two therapies has been done to analyze the efficacy of each care standards for patients who are suffering from cancer. The estimation of cost-effectiveness and clinical benefits of the two regiments have enabled the usage of test outcomes. The predictors involve screening, disease progression, and outcome measures. The decision tree provided above explores innovative approaches to enhance patient flow and avoid overcrowding. A potential strategy used here pertains to data mining with the help of multiple machine learning approaches to predict rates of admissions. Screening for cancer with the new test thus utilizes resources in a cost-effective manner.

Quality-Adjusted Life Year (QALY) Measurement

Figure 5.1: Decision tree with ICER and sensitivity

(Source: Given by Researcher)

The model is sensitive to the parameters of costs and availability of resources. Parameters like resources costings ensure the usage of appropriate costs for the screening and diagnostics. In addition to this, the availability of resources can also determine the flow of the process. In addition to the above-mentioned parameters, the prevalence of clinical trends in the sample also gives rise to certain sensitivity issues. The outcomes obtained from both the tests are laid out for a comparative appraisal. This framework is thus used to quantify outcomes and attain achievement.

Early detection of suspected cervical cancer patients: It is essential that women should have proper and adequate information regarding the symptoms of cervical cancer. Wright et al. (2015:189-197) commented that irregular or heavy menstruation, groin and pelvic pain are some of the key symptoms of cervical cancer. The affected patients should identify these primary symptoms and visit their physicians for early detection. Campos et al. (2015:2208-2219) added that early detection of any cancer results in the proper health status of the patients.

Pap smear test: This test helps in early and proper detection of the prevalence of any pre-cancerous growth near the cervix. In this test, clinicians collect cells from the cervical area and carry out pathological tests. This test helps in analyzing the presence or growth of cancerous cells in the cervix.

Conclusion 

Early detection of cervical cancer helps in improving the health status of patients. In case, pre-cancerous growth is detected early then cervical cancer could be effectively prevented from occurring among the patients. Therefore, a proper analysis of the symptoms and their identification is necessary to treat the condition.

Reference List

Ferri, F. F. (2017) Ferri's Best Test E-Book: A Practical Guide to Laboratory Medicine and Diagnostic Imaging E-Book. Amsterdam: Elsevier Health Sciences.

Malanga, G. A., & Mautner, K. (2016) Musculoskeletal Physical Examination E-Book: An Evidence-Based Approach

Blatt, A. J., Kennedy, R., Luff, R. D., Austin, R. M., & Rabin, D. S. (2015) Comparison of cervical cancer screening results among 256,648 women in multiple clinical practices. Cancer Cytopathology, 123(5), 282-288.

Campos, N. G., Castle, P. E., Wright Jr, T. C., & Kim, J. J. (2015) Cervical cancer screening in low?resource settings: A cost?effectiveness framework for valuing tradeoffs between test performance and program coverage. International journal of cancer, 137(9), 2208-2219.

Canfell, K., Caruana, M., Gebski, V., Darlington-Brown, J., Heley, S., Brotherton, J., ... & Wrede, C. D. (2017) Cervical screening with primary HPV testing or cytology in a population of women in which those aged 33 years or younger had previously been offered HPV vaccination: Results of the Compass pilot randomized trial. PLoS medicine, 14(9), e1002388.

Canfell, K., Saville, M., Caruana, M., Gebski, V., Darlington-Brown, J., Brotherton, J., ... & Castle, P. E. (2018) Protocol for Compass: a randomized controlled trial of primary HPV testing versus cytology screening for cervical cancer in HPV-unvaccinated and vaccinated women aged 25–69 years living in Australia. BMJ Open, 8(1), e016700.

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Hariri, S., Bennett, N. M., Niccolai, L. M., Schafer, S., Park, I. U., Bloch, K. C., ... & Abdullah, N. (2015) Reduction in HPV 16/18-associated high-grade cervical lesions following HPV vaccine introduction in the United States–2008–2012. Vaccine, 33(13), 1608-1613.

Simms, K. T., Hall, M., Smith, M. A., Lew, J. B., Hughes, S., Yuill, S., ... & Canfell, K. (2017) Optimal management strategies for primary HPV testing for cervical screening: cost-effectiveness evaluation for the National Cervical Screening Program in Australia. PloS one, 12(1), e0163509.

Online Articles

Arrossi, S., Thou art, L., Herrero, R., Campanera, A., Magdaleno, A., Cuberli, M., ... & EMA Study Team. (2015) Effect of self-collection of HPV DNA offered by community health workers at home visits on the uptake of screening for cervical cancer (the EMA study): a population-based cluster-randomized trial. The Lancet Global Health, [Online] 3(2), e85-e94. Available from https://www.sciencedirect.com/science/article/pii/S2214109X14703547 [Accessed 18th September 2018]

Crowe, E., Pandeya, N., Brotherton, J. M., Dobson, A. J., Kisely, S., Lambert, S. B., & Whiteman, D. C. (2014) Effectiveness of quadrivalent human papillomavirus vaccine for the prevention of cervical abnormalities: the case-control study nested within a population-based screening programme in Australia. BMJ, [Online] 348, g1458. Available from https://www.bmj.com/content/348/bmj.g1458.abstract [Accessed 19th September 2018]

Sabatino, S. A., White, M. C., Thompson, T. D., & Klabunde, C. N. (2015) Cancer screening test the use-United States, 2013. MMWR. Morbidity and mortality weekly report, [Online] 64(17), 464-468. Available from https://europepmc.org/articles/pmc4584551 [Accessed 16th September 2018]

Wright, T. C., Stoler, M. H., Behrens, C. M., Sharma, A., Zhang, G., & Wright, T. L. (2015) Primary cervical cancer screening with human papillomavirus: end of study results from the ATHENA study using HPV as the first-line screening test. Gynecologic oncology, [Online] 136(2), 189-197. Available from https://www.sciencedirect.com/science/article/pii/S0090825814015492 [Accessed 15th September 2018]

Rho Cervical Cancer, (2018). Screening and treatment, Viewed on 18th September 2018 < https://www.rho.org/screening.htm>