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Which of our economic models is right for you?

We look at the different economic models available for project evaluations.

Healthcare commissioners and providers are faced with decisions on how and where to allocate their resources every day, and it’s crucial that they have the tools needed to make informed choices.

At ICHP, our extensive network of members in North West London, our collaborators and our wide range of clients mean that we are regularly approached to evaluate health-related products ranging from medicines, digital health and medical technology, to algorithms, new models of care, and interventions including surgical procedures.

Our health economic evaluations can help commissioners and providers to make decisions on these kinds of products by demonstrating whether they deliver value for money compared to other relevant options, and whether they are affordable. This requires a framework which combines and analyses large volumes of often complex evidence in a systematic way.

Health economic models synthesise and extrapolate evidence typically on cost and effectiveness, looking beyond a trial period, and also take into account the various types of uncertainty that may be encountered during the evaluation process such as parametric, methodological or structural uncertainty[i].

There may be multiple model parameter estimates from several data sources. This introduces uncertainty in the estimates, the effects of which will need to be explored through sensitivity analysis (SA). SA also includes presenting alternative scenarios on treatment pathways. These scenarios are informed through stakeholder engagement, where our contacts with clinical experts from across our network help us identify the relevant clinical pathways, so that our models accurately reflect real practice.

Types of economic evaluations

There are various types of economic evaluations available. Options include a budget impact model (BIM), cost-consequence analysis, cost-benefit analysis, cost-effectiveness analysis (CEA) and cost-utility analysis (CUA)[ii]. Each evaluation has its value and which one we select to use depends on each client’s specific requirements.

The potentially high economic impact associated with introducing new medicines into the healthcare system mean that all evidence on patient outcomes/effectiveness and costs is typically presented in a CEA/CUA framework and modelled over a lifetime horizon against all relevant comparators, offering an in-depth assessment of value for money.

NICE[iii] offers clear guidelines on assessing the clinical and economic impact of technologies and has recently published an evidence standard framework for assessing digital health technologies. This framework outlines a three-tier approach from a basic analysis (requiring a BIM) and a low financial commitment (requiring a cost-consequence analysis and BIM) up to devices with a high financial commitment (requiring a CUA and BIM).

The CEAs/CUAs are usually more complex than the BIM but the BIM remains one of the most popular models we are asked to develop. It is a financial approach which assesses the consequences of adopting a new technology in a specific setting. The horizon is usually three to five years, unlike CEA models, which are modelled over a lifetime horizon. The BIM takes into account the relevant patient cohort, size of the population, speed of uptake of the new technology and current and new market shares/treatment mix. BIMs can be presented alongside other economic evaluations.

Our access to real-world data (both national data and local North West London data) enables us to provide a framework for users into which they can input the relevant data for their local population. Flexible user inputs allow the model to be adapted to the setting, informing the decision maker (and budget holder) about the financial impact of adopting the new technology.

We used this approach in our recent atrial fibrillation (AF) budget impact model. This uses published data about primary care performance against QOF targets, together with estimated prevalence data, to compare current management of AF with optimal goals. It allows the potential clinical and cost consequences of increased screening and management of AF to be estimated at both CCG and STP level, allowing commissioners to make informed decisions about the best approach to caring for AF patients in their area.

By Dr Wayne Smith, Health Economic Lead at ICHP.

[i] Briggs, A. (2000) Handling Uncertainty in Cost-Effectiveness Models. Pharmacoeconomics, 17, 479-500.

[ii] All definitions are presented on page 12-13 of the guideline of European Network for HTA available at: https://eunethta.eu/wp-content/uploads/2018/01/Methods-for-health-economic-evaluations-A-guideline-based-on-current-practices-in-Europe_Guideline_Final-May-2015.pdf

[iii] NICE Evidence Standards Framework for Digital Health Technologies (March 2019). See Table 9 page 30.