Reference no: EM132394758

23787 Health Technology Assessment - University of Technology Sydney, Australia

Subject learning objectives - Upon successful completion of this subject students should be able to:

1. Explain the principles and methods of economic evaluation for new health technologies.

2. Discuss the role of health technology assessment in reimbursement decision making for health care in Australia and internationally.

3. Critically appraise data for health technology assessment.

4. Evaluate the different approaches to constructing economic models for HTA.

5. Apply methods for estimating costs and benefits of health care interventions.

Assessment Task -

The aim of this assessment is to construct a simple decision tree diagram to assess the cost- effectiveness of a new cervical screening test when compared to the current screening test. The assessment task comprises a series of structured stages, including the preparation of a written brief health technology assessment report.

Your completed assessment task should have

1. An executive summary that represents a brief health technology assessment report. This should summarise your answers and be structured under the following headings:

• Objectives
• Methods
• Data
• Results
• Key areas of uncertainty
• Discussion
• Recommendation

The executive summary should be no more than 500 words.

2. A structured report that presents the answers to each of Parts One to Six of the task, including calculations where required.

Part One: Calculating the accuracy of two test alternatives

Screening tests for cervical cancer aim to identify pre-cancerous changes in the cervix that could develop into cervical cancer. If the pre-cancerous tissues are identified early and removed, then cervical cancer can be prevented from developing.

The Comparator - In the conventional Pap smear, the doctor collecting the cells smears them on a microscope slide and applies a fixative. This slide is then sent to a laboratory for evaluation. Studies of the accuracy of conventional (current) Pap smear tests report:

• Sensitivity 70%
• Specificity 94%

The New Test - The new test works in exactly the same way as the current test, however the manufacturer believes that the sensitivity of the new test is better. Below are the results of a cohort study that tested the new cervical screening test. Note that all women were 30 years of age when tested.

New Test	Disease Status		Total
	Cervical cancer (+ve)	Cervical cancer (-ve)
Test (Positive)	53	35
Test (Negative)	7	580
Total

A) For the new cervical screening test define the following, and include the number of individuals in each group.

• True positive
• False positive
• True negative
• False negative

B) Calculate the sensitivity and specificity for the new test.

C) Compared with the current test, the new test was evaluated using a different cohort of women and in a different laboratory. Does this influence the sensitivity and specificity of the new test?

Part Two: Construct a decision tree

Your task is to assess the cost-effectiveness of screening women when they reach the age of 30. We also assume that everyone who is invited to participate in the screening program receives a cervical screening test (i.e. the uptake rate of the test is 100%).

Draw a decision tree to determine whether the new cervical screening test is more cost-effective than the current test. To do this you need to create a decision node with the option to accept the new test or the current test. For each test, the terminal nodes should reflect the possible outcomes of the test result (e.g. True positive etc...). AT THIS POINT YOU DO NOT NEED TO POPULATE THE TREE - THAT WILL FOLLOW IN THE SUBSEQUENT STEPS. Please provide a picture of your unpopulated tree.

Part Three: Estimating the benefit of testing

To populate the decision tree, we need to estimate the benefits and costs of each test option. The benefits of screening are measured in terms of quality adjusted life years (QALYs) gained (i.e. quality- of-life multiplied by the number of years in that health state).

Utility score - A time-trade off study conducted on the same cohort of women that received the new test demonstrated that:

• The average utility in the non-cancer group (test negative) was 0.92.
• The average utility in the non-cancer group (test positive) was 0.90 (slight reduction in utility due to further investigations and concern of possible cancer)
• The average utility in the cancer group (not detected by the test) was 0.60 (This reduced utility is due to the side-effects of treatment and the impact of the disease).
• The average utility in the cancer group (detected by the test and treated early) is 0.85 (there is a slight reduction in quality of life due to early treatment).

Survival - Long-term registry data were used to estimate the additional survival (note that this is the additional survival beyond 30 years of age, which is the age when a person would be screened in this model)

• The average survival of a 30 year old woman with cervical cancer (not detected early) is an additional 7 years.
• The average survival of a 30 year old woman with cervical cancer that is detected early and treated (i.e. detected with a positive test results) is an additional 40 years.
• For all other 30 year old women (no cancer) the average survival is an additional 40 years.

A) Calculate the average QALYs for individuals with the following possible test outcomes:

• True positive
• False positive
• True negative
• False negative

B) In this model, all outcomes (costs and benefits) are undiscounted. Why do we discount future costs and benefits? Why might discounting costs and benefits at the same rate penalize preventative health programs?

Part Four: Estimating Costs

The tables below were taken from a longitudinal cohort study of women that participated in the current screening program. The unit costs are provided in Table 1. Table 2, contains an inventory of all the resources used, on average, by an individual depending upon their test result.

Combine the information from Tables 1 and 2 below to generate the total cost of each screening outcome. Do this for both the current test and the new test scenarios.

Table 1: Unit costs

Description	Cost
Current test	$50
New Test	$400
GP appointment	$35
Further examination - No treatment	$500
Further examination - Early treatment	$1,000
Delayed treatment	$50,000

Table 2: Resources use for each possible alternative

		Current test	New Test	GP visit	Further	Further	Delayed treatment
Current test	True Positive	1		2		1
	False positive	1		2	1
	True negative	1		1
	False negative	1		1			1
New Test	True Positive		1	2		1
	False positive		1	2	1
	True negative		1	1
	False negative		1	1			1

Part Five: Cost-utility analysis

You should now have the following information:

• Accuracy of the current and new cervical screening tests.
• A decision tree that reflects the possible outcomes of both tests.
• An estimate of the QALYs gains for each alternative.
• An estimate of the resource use (cost) of each alternative.

The final information that you need to complete the analysis is the prevalence of cervical cancer in this population. In this example, we are screening women 30 years of age; the prevalence of cervical cancer in this cohort is 7 in 10,000 or (0.0007).

A) Complete Table 3: Model parameters using the information from Part One to Part Four.

Table 3: Model Parameters

Parameter description	Current Test	New Test
Prevalence of cervical cancer	0.0007	0.0007
Sensitivity of test	0.70
Specificity of test	0.94
Cost - True Positive
Cost - False Positive
Cost - True Negative
Cost - False negative
QALYs - True Positive
QALYs - False Positive
QALYs - True Negative
QALYs - False negative

B) You now need to combine this information into your decision tree to determine the cost-effectiveness of the new test relative to the current test. Provide your answer as an incremental cost-effectiveness ratio (ICER; i.e. cost/QALY gained). Provide the diagram of your populated decision tree at this stage.

• Hint: Remember that you need to calculate the expected value (costs and QALYs) of each alternative before you can estimate the cost-effectiveness. It is easier to calculate the expected value if you start at the end of the tree, rather than the beginning (i.e. you need to roll-back the decision tree - see lecture notes for example)

C) If the decision maker has set an explicit threshold of $50,000 / QALY gained, would you say the new test is cost-effective? Explain your answer.

Part 6: Sensitivity Analysis

The decision maker would like you to determine the cost-effectiveness of the new test in a population of women with a family history of cervical cancer. In this high risk cohort of women, the prevalence of cervical cancer is 6 in 1,000 (0.006).

A) Calculate the ICER of the new test relative to the current test in this high-risk population of women.

B) Why do you think the cost-effectiveness of the new test is sensitive to the prevalent risk of cervical cancer in the population?

C) In the original model (hint: assuming prevalence = 0.0007), we assumed a 20 min GP appointment costs $35. However, an audit of general practices conducting the new test shows that 80% of GPs charge patients a double appointment (2 x 20mins) per visit. NOTE: THE DOUBLE GP APPOINTMENT CHARGE APPLIES TO ONLY THE NEW TEST. How does this change your ICER? Explain your answer.

D) Is the model sensitive to any other parameters? Please justify your answer.

Attachment:- Health Technology Assessment File.rar