X Close

Janssen UCL Medicinal Personalization

Home

Menu

Case Study 2

Customising scenario assessments to characteristics of individual Hep C treatment centres.
Download (Tool 1 – Liverpool)

As an illustration of the functionality of the modelling tool, we present a case study whereby a number of parameters are changed.

1.  Selecting patient base

We assume that we are carrying out this case study for a fictional Hep C treatment centre that is the city’s only treatment centre, so we specify 100% next to Liverpool PCT in the Population selection sheet. Accordingly, all Hep C referrals in the Liverpool PCT will end up at this centre.

Screen Shot 2013-11-07 at 18.18.36

We note that the over-18 population is 363,075

2.1  Clinic assumptions input sheet

In this next step, we proceed to the Assumptions input sheet and assume that HCV prevalence rate for Liverpool is slightly higher than for the patient population served by the Royal Free, 0.75% as opposed to 0.5%. The diagnosis for the existing people[C1]  is slightly higher than the national average (60% as opposed to national average 50%), but for the newly diagnosed it is slightly lower (at 55% as opposed to national average 64%).

Proportion who develop chronic HCV and who are genotype 1 are at the national average, yet a higher proportion than national average is offered treatment at this Liverpool clinic (70%).

Likewise we make changes to those that have treated or not treated before (40% to 60%, slightly different to national averages).

Furthermore, more people are referred to treatment and their annual transition rates (e.g. when sustained virological response is achieved) differ from national averages as well.

CS2.2.1

2.2  Clinic assumptions input sheet

In the next step, we assume that clinicians at this centre prescribe mostly Telaprevir therapy as opposed to Boceprivir by the ratio of 66% to 34% (At the Royal Free this ratio is 50%-50%). We also assume that the proportion of patients with cirrhosis as opposed to those with mild/moderate liver disease is higher at the Liverpool clinic.

CS2.2.2

2.3  Clinic assumptions input sheet

In this next step, we assume that the costs of drug therapy are slightly lower than the national average by about 10%.

CS2.2.3

3.  Sensitivity analysis

The sensitivity analysis for our case study, which assesses the impact of variations of individual input variables on the overall costs calculated in our model is automatically calculated based on our assumptions in a separate worksheet for this case study.

CS2.3

4.  IDYLLA value

When we incorporate the costs associated with installing a new POC diagnostic device, we note that for this hypothetical case study, Liverpool will find it cost-effective to invest in and introduce a rapid turnaround diagnostics service in Hep C care both in terms of the potential annual savings on viral load testing  (£34,989) and on drug costs (£186,578).

Note that these savings persist despite the hypothetical 10% discount from UK list prices on drug costs. In addition, unlike our Royal Free example presented in Case Study 1 where a loss was evident, Liverpool is able to achieve greater savings on viral load testing due to the larger patient population it serves (and hence the greater number of tests it needs to run) and the higher rate it pays per VL test, which is set at the national average costs for viral load testing.

CS2.4